Stimulation parameters in deep brain stimulation (DBS) of the subthalamic nucleus for Parkinson's disease (PD) are rarely tested in double‐blind conditions. Evidence‐based recommendations on optimal stimulator settings are needed. Results from the CUSTOM‐DBS study are reported, comparing 2 pulse durations.A total of 15 patients were programmed using a pulse width of 30 µs (test) or 60 µs (control). Efficacy and side‐effect thresholds and unified PD rating scale (UPDRS) III were measured in meds‐off (primary outcome). The therapeutic window was the difference between patients’ efficacy and side effect thresholds.P = ·0009) and the efficacy (UPDRS III score) was noninferior (P = .00008).The therapeutic window was significantly larger at 30 µs than 60 µs (Subthalamic neurostimulation at 30 µs versus 60 µs pulse width is equally effective on PD motor signs, is more energy efficient, and has less likelihood of stimulation‐related side effects. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. The efficacy and side effect profile of deep brain stimulation (DBS) critically depends on the electrical activation of specific neural elements within a small brain volume.The CUSTOM‐DBS study was designed to address the need for an evidence‐based update of DBS programming guidelines,PD patients bilaterally implanted with a Vercise DBS system  in the STN for at least 3 months were included at 4 German centers. All patients had a Unified Parkinson's Disease Rating Scale (UPDRS) subset III score of ≥30 in the preoperative meds‐off state, and ≥30% symptom reduction in the meds‐off state (UPDRS III score) by DBS. To ensure sufficient rigidity to reliably measure a threshold, a meds‐off rigidity score ≥ 2 was required in the evaluated arm (UPDRS III). Patients were excluded if severe tremor would interfere with measurements, defined as meds‐off resting and/or action tremor score ≥3 on the evaluated side (UPDRS III). Patient demographics are shown in Supplementary Table 1.ClinicalTrails.gov (NCT01896115).All sites obtained ethics committee approval. Written informed consent was given by all patients before study inclusion. The study is registered with Test conditions were administered in random order with masking of patients and the examiner to treatment assignment. Random permutations of the treatment sequence, stratified by site with a block size of 4, were pregenerated along with a set of mutually distinct identification codes. To mask the randomization assignment, each site only received the codes, each sealed within a numbered envelope to be opened sequentially as patients enrolled into the study. The nonblinded programmer controlled the pulse width and electrode settings according to the randomization sequence matched to the code using a computer positioned out of the examiner's sight.Primary data collection occurred in a single programming visit. Patients arrived in the practically defined meds‐off state . The clinical DBS effect was washed out by turning the device off for at least 1 hour. Testing was started when the patient and examiner agreed that a representative off‐state was reached. DBS was then turned back on contralateral to the clinically more affected side of the body. To evaluate pulse‐width effects, cathodic stimulation was delivered through the best single therapeutic electrode (monopolar against generator case), fixed at 130 Hz; pulse width was randomly assigned to either test (30 µs) or control (60 µs) settings. At each setting, amplitude was gradually increased until a side effect threshold was measured (defined as the minimum amplitude for any side effect persisting after 2 minutes of stimulation). Rigidity was evaluated 0.1 mA below the side effect threshold to assess the maximum rigidity control possible at that setting. The efficacy threshold was defined as the amplitude at which rigidity reoccurred by gradually decreasing the stimulation in steps of 0.1 mA.Efficacy was measured in 2 ways after at least 15 minutes of continuous stimulation at the previously determined efficacy threshold for each setting: UPDRS III evaluation , and an objective, quantitative assessment of tremor and bradykinesia using a finger‐worn motion sensor . In the latter assessment, patients performed 3 tasks while wearing a motion sensor on the index fingertip: rest tremor assessment, finger‐tapping task, and rapidly alternating‐movement task. The study design is summarized in Fig Efficiency of neurostimulation was calculated as the total charge delivered per pulse (Q = I * pulse width) at the efficacy threshold.t test at an alpha level of 0.025, applying the Bonferroni correction to preserve the family‐wise error rate. A margin of 5 points, representing the minimal clinically relevant difference,The coprimary endpoints of this study were (1) superiority of the therapeutic window size and (2) noninferiority of stimulation efficacy (UPDRS III) on the most affected side of contralateral DBS with 30 µs compared to 60 µs pulse width. The 2 endpoints were each assessed using a 1‐sided paired Sample size calculations suggested that the first coprimary endpoint (superiority of therapeutic window size) could be met after 16 participants, whereas the noninferiority endpoint would require 40 enrolled participants. Therefore an interim analysis was planned after enrollment of the first 16 patients and confirmed that both coprimary endpoints could be met at this stage. Therefore, the study could be discontinued, as per protocol, after the enrollment of 16 participants.Of 16 enrolled participants, 1 did not tolerate reprogramming and withdrew before any data could be collected. Therefore, 15 patients completed the study.P = .0009; Fig P = .00008) or by lateralized motor score for the evaluated side . Noninferiority of the stimulation efficacy at 30 µs was further confirmed by kinematic measures of rest tremor, finger‐tapping, or rapidly alternating movements .For these 15 participants, stimulation at 30 μs resulted in a significantly larger therapeutic window than 60 μs  during an acute stimulation challenge has previously been used as the primary efficacy endpoint in DBS studiesDespite these limitations, few controlled studies are aimed at achieving optimization of DBS programming, and this is the first double‐blind assessment of the effect of a shorter pulse width and 1 of only a handful of DBS programming studies that have ever been conducted in a double‐blind condition.In conclusion, stimulation using a shorter than currently recommended pulse width may be more efficient at achieving therapeutic efficacy and less likely to reach a side effect threshold. This may translate into a fundamentally new basic parameter setting for patients with DBS in PD.1) Research project: A. Conception, B. Organization, C. Execution; 2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; 3) Manuscript: A. Writing of the first draft, B. Review and Critique.F.S.: 1B, 1C, 3A, 3BL.T.: 1A, 1B, 1C, 3A, 3BA.K.: 1B, 1C, 3A, 3BA.S.: 1B, 1C, 3A, 3BM.M.R.: 1B, 1C, 3A, 3BA.D.K.: 1B, 1C, 3A, 3BM.T.B.: 1B, 1C, 3A, 3BV.V.‐V.: 1B, 1C, 3A, 3BJ.H.: 1B, 1C, 3A, 3BC.R.: 1B, 1C, 3A, 3BS.J.G.: 1B, 1C, 3A, 3BA.‐S.M.: 1B, 1C, 3A, 3BS.L.: 1B, 1C, 3A, 3BS.C.: 1B, 1C, 3A, 3BL.M.: 1B, 1C, 3A, 3BJ.V.: 1A, 1B, 1C, 3A, 3BF.S. reports personal fees from Boston Scientific, personal fees from Medtronic, and personal fees from St. Jude Medical outside the submitted work. L.T. reports grants and personal fees from Bayer Healthcare, grants, personal fees and nonfinancial support from UCB Pharma GmbH (UCB) Schwarz Pharma, grants, personal fees and nonfinancial support from Archimedes Pharma, grants, personal fees and nonfinancial support from TEVA Pharma, grants, personal fees and non‐financial support from Lundbeck Pharma, personal fees from Medas Pharma, grants, personal fees and non‐financial support from Desitin Pharma, personal fees and non‐financial support from GlaxoSmithKline, personal fees and non‐financial support from Orion Pharma, grants, personal fees and non‐financial support from Abbvie, personal fees from TAD Pharma, grants from German Research Foundation, grants from German Ministry of Education and Health, grants from Manfred and Ursula Müller Foundation, grants from Klüh Foundation, grants from Hoffnungsbaum e.V., grants from Neurodegeneration with Brain Iron Accumulation Disorders Society USA, grants from Deutsche Parkinson Vereinigung, and grants from zur Rose Pharma outside the submitted work. A.K. reports other from Boston Scientific, grants and other from Medtronic, grants and other from St Jude Medical, other from Ipsen Pharam, outside the submitted work. A.S. reports personal fees from Medtronic, personal fees from St Jude Medical, personal fees from UCB, personal fees from MEDA Pharma GmbH Pharma, grants and personal fees from Teva Pharma, personal fees from GlaxoSmithKline, personal fees from Novartis, personal fees from Abbvie, outside the submitted work. M.M.R. has been a member of the advisor board of Medtronic; has received grant support from Boston Scientific, St. Jude and TEVA; and has received honoraria for speaking from Medtronic. A.D.K. reports grants from Abbvie, outside the submitted work. M.T.B. reports grants from Medtronic Inc., outside the submitted work. V.V.‐V. reports grants and non‐financial support from Medtronic, Boston Scientific, SAPIENS, and St. Jude Medical, and personal fees from Medtronic and St Jude Medical, as well as a grant and nonfinancial support from Boston Scientific outside the submitted work. S.J.G. reports personal fees from Boston Scientific, personal fees from Medtronic, and nonfinancial support from Actelion outside the submitted work. S.L., S.C., and L.M. report employment with Boston Scientific. J.V. reports grants and personal fees from Medtronic, personal fees from Allergan, personal fees from Merz, personal fees from UCB, personal fees from Abbvie, personal fees from TEVA, personal fees from Zambon, and personal fees from Bial outside the submitted work. J.H., C.R., A.‐S.M. have nothing to disclose.Additional Supporting Information may be found in the online version of this article at the publisher's website.Supplementary Information Figure 1Click here for additional data file.Supplementary Information Figure 2Click here for additional data file.Supplementary Information Table 1Click here for additional data file.Supplementary Information 1Click here for additional data file.Supplementary Information 2Click here for additional data file.


Sedentary behavior is considered an independent cause of cardio-metabolic diseases, regardless of physical activity level and obesity. Few studies have reported the association between leisure sedentary time and cardio-vascular diseases in terms of occupation.We performed a cross-sectional study using data from the Korean Community Health Survey (KCHS) for 240,086 participants assessed in 2011 and 2013. Occupation was categorized into four groups: farmer or fisherman, laborer, and soldier (Group I); service worker, salesperson, technician, mechanic, production worker, and engineer (Group II); manager, expert, specialist, and clerk (Group III); and unemployed (Group IV). Leisure sedentary time was divided into five groups: 0 h, 1 h, 2 h, 3 h, and 4+ h. The association between leisure sedentary time on weekdays and hypertension/diabetes mellitus/hyperlipidemia for different occupations was analyzed using simple and multiple logistic regression analyses with complex sampling.In Groups I, II and III, no length of sedentary time was associated with hypertension, and only 3 h or 4+ h of sedentary time was associated with diabetes mellitus and hyperlipidemia. Group IV showed a significant association with hypertension and diabetes mellitus for the 2 h, 3 h, and 4+ h sedentary times.The unemployed are more susceptible than other occupation groups to cardio-metabolic diseases when leisure time is sedentary.The online version of this article (doi:10.1186/s12889-017-4192-0) contains supplementary material, which is available to authorized users. Sedentary behavior is generally defined as any waking behavior characterized by an energy expenditure ≤1.5 metabolic equivalent test score (METs) while in a sitting or reclining position , diabeteFor adults, sedentary time could be divided into workplace sedentary time, including commute time, and leisure sedentary time. Adults usually spend approximately 1/3 of their weekday time (approximately 1/2 of their weekday awake time) working. It is expected that the physical activity or sedentary behavior patterns of each occupation would be different. Because of the relatively large proportion of working hours in a 24-h day, the differences in working behaviors might affect cardio-metabolic disease differently. However, it is difficult to accurately measure the differences in physical activity and sedentary behavior in the workplace , and fewIn contrast to time spent in the workplace, the use of leisure time can be changed relatively easily through individual effort, and leisure sedentary time is more predominant than workplace sedentary time (~60%) . TherefoThis study was approved by the Institutional Review Board of Korea Centers for Disease Control and Prevention (IRB No. 2011-05CON-04-C and 2013-06EXP-01-3C). Written informed consent was obtained from all participants prior to the survey.tong/ban/ri) was selected as a primary sample unit, which was selected according to the number of households in the area using a probability proportional to the sampling method. In the second stage, the number of households in the selected sample tong/ban/ri was identified to create a household directory. Sample households were selected using systematic sampling methods. This process was used to ensure that the sample units were representative of the entire population [This study is a cross-sectional study using data from the Korean Community Health Survey (KCHS). The KCHS conducted in 2011 and 2013 was analyzed. The data were collected by the Centers for Disease Control and Prevention of Korea. The survey was administered through face-to-face, paper-assisted personal interviews between trained interviewers and respondents. The sample size for the KCHS was 900 subjects in each of 253 community units, including 16 metropolitan cities and provinces. The KCHS used a two-stage sampling process. In the first stage, a sample area ; participants who did not complete the income record ; participants who did not report height or weight (3266 participants); and participants who had incomplete data for educational level, occupation, smoking, alcohol consumption history, sleep hours, stress level, physical activity, sedentary time, hypertension, diabetes mellitus, and hyperlipidemia (1554 participants). Ultimately, 240,086 participants  were included in this study . This MPA was divided into three groups: 0 min/week (inactive); 1–149 min/week (insufficiently active); ≥ 150 min/week (sufficiently active) .Occupation was classified into ten standard Korean occupations: manager; expert; specialist; clerk; service worker; salesperson; farmer or fisherman; technician, mechanic, production worker, or engineer; laborer; and soldier. Unemployed participants comprised an eleventh group. Then, it was categorized into four groups according to their possible physical activities: farmer or fisherman, laborer, and soldier (Group I); service worker, salesperson, technician, mechanic, production worker, and engineer (Group II); manager, expert, specialist, and clerk (Group III); and unemployed (Group IV). Although we did not measure each occupation group’s physical activity level or sedentary time during working hours, our grouping of theses occupations is consistent with common sense and other study results , 14, 18.Using the methods recommended by the Organization for Economic Cooperation and Development  , four BMI groups were established: underweight, < 18.5; healthy, ≥ 18.5, < 25; overweight ≥25, < 30; obese, ≥ 30.To evaluate the sedentary time according to occupation, the participants less than 30 years of age or over 60 years of age were excluded from this study. These exclusion limits were based on the theory that these age groups could be unemployed due to college enrollment, job-seeking activities or retirement. Participants under 110 cm or 30 kg were excluded from this study. Using the World Health Organization’s  internatTo explore the influence of educational level, uneducated participants, and those who had graduated only from elementary or middle schools, were assigned to the “low” education group; high school graduates comprised the “middle” group, and junior college graduates, college and graduate school graduates formed the “high” group.Smoking status was divided into three groups: non-smoker; past smoker; and current smoker. The past smokers who had quit smoking less than one year prior to the study were included in the current smoker group. Alcohol consumption was divided into the following four categories: None; ≤ 1 time a month; 2–4 times a month; ≥ 2 times a week. Amount of sleep was divided into four groups: 3–5 h per day; 6 h per day; 7 h per day; and ≥8 h per day. Participants who sleep less than three hours per day were excluded from this study (0.2% of the participants were excluded). The participants were asked if they usually feel stress, and stress levels were divided into the following four groups: no stress; some stress; moderate stress; and severe stress.To measure sedentary time, the question asked was, “How much time did you spend per day sitting to watch TV, play games, use the internet, or do other things, during your leisure time on weekdays during the past week?” The participants chose from five possible answers: < 1 h (0 h); ≥ 1 h, < 2 h (1 h); ≥ 2 h, < 3 h (2 h); ≥ 3 h, < 4 h (3 h); ≥ 4 h (4+ h). Weekend sedentary time was also surveyed. Because both weekday and weekend sedentary times were surveyed as categorical variables, it was not possible to combine them into one category. Therefore, we chose only weekday sedentary time for this study.The participants were questioned about their histories of hypertension, diabetes mellitus, and hyperlipidemia, and those who reported a history of any of these diseases, as diagnosed by a medical doctor, were recorded as positive. The questionnaire was described in the Additional file Differences in mean age among occupation groups were compared using linear regression analysis with a complex sampling test. The rate differences of gender, income level, BMI group, educational level, occupation, alcohol consumption, smoking history, sleep hours, stress level, hypertension, diabetes mellitus, and hyperlipidemia history were compared by using the Chi-square test with Rao-Scott correction.P-values lower than 0.05 were considered to indicate significance. The AOR and 95% confidence interval (CI) for hypertension, diabetes mellitus, and hyperlipidemia were calculated. All results are presented as weighted values. The results were analyzed statistically using SPSS ver. 21.0 .To identify associations between sedentary time and hypertension, diabetes mellitus, and hyperlipidemia, simple and multiple logistic regression analyses with complex sampling were used. In the multiple logistic regression analysis, age, sex, income, obesity, education, alcohol, smoking, stress, physical activity, sleep, and leisure sedentary time were adjusted as the confounders. For the subgroup analysis according to the occupation groups (Groups I to IV), multiple logistic regression analysis with complex sampling was used. Two-tailed analyses were conducted, and The total 240,086 participants were distributed across the occupation Groups I, II, III, and IV as follows: 44,116 (18.4%), 75,912 (31.6%), 63,213 (26.3%), 56,845 (23.7%), respectively. Group I  showed the highest mean age, relatively lower income and educational level, and higher MPA. Group II  exhibited middle educational level and the highest smoking rate. Group III  displayed the highest income and educational level, and relatively less sleep time. Group IV (unemployed) showed a higher female rate, the lowest smoking and alcohol consumption, relatively lower MPA, and longer sleep time , and 4+ h  was linked to hyperlipidemia (P = 0.001). In Group II, compared with 0 h sedentary time, only 4+ h  was related to diabetes mellitus (P < 0.001). In Group III, compared with 0 h sedentary time, 3 h  was related to diabetes mellitus (P = 0.002) and 3 h  and 4+ h  were associated with hyperlipidemia (P < 0.001). In Group IV, compared with 0 h sedentary time, 2 h , 3 h  and 4+ h  were linked to hypertension (P < 0.001); 2 h , 3 h , and 4+ h  were related to diabetes mellitus (P = 0.007); 3 h , and 4+ h  correlated with hyperlipidemia  and 4+ h  of sedentary time was associated with diabetes mellitus  and 4+ h alone  leisure sedentary time. Warren TY, et al. reported that sedentary time did not increase the risk of cardiovascular disease in physically active participants, whereas it increased this risk for those who were inactive ; howeverGroup III is the most highly educated group in this study, and most sedentary jobs require higher educational levels . TherefoGroup IV, which comprised the unemployed population, showed a significant association between cardio-metabolic diseases even with 2 h leisure sedentary time. In other studies, unemployed persons showed longer sedentary time and lower physical activity than any other occupation groups , 28. TheThis study has various advantages. It compared the association between leisure sedentary time and cardio-metabolic diseases among different occupation groups within a large population-based survey for the first time. Sampling was weighted by statisticians to reflect the mother population. We adjusted for MPA, sleep time, and obesity, which could act as confounding factors , 30, wheDespite these advantages, this study has several limitations. First, this study used self-reported measurement of sedentary behavior and MPA. Self-reported measurements could be inaccurate due to recall bias. However, objective measurement using an accelerometer also has bias owing to incomplete data and measurement errors . In someThis study indicates that the unemployed could be more susceptible to cardio-metabolic diseases when using leisure time for sedentary behavior than other occupation groups. Therefore, we should pay more attention to the leisure time use of the unemployed.Additional file 1:The Questionnaire of the study. The surveyed questionnaire was described. (DOCX 16 kb)Additional file 2:Odds ratios of sedentary time for hypertension, diabetes mellitus, and hyperlipidemia using multiple logistic regression analyses with complex sampling. In other statistical model, the odd ratios of sedentary time were described. (DOCX 20 kb)Additional file 3:Subgroup analysis of adjusted odds ratios of sedentary time for hypertension, diabetes mellitus, and hyperlipidemia according to the occupation groups in female using multiple logistic regression analyses with complex sampling. This data shows the odd ratios of female groups. (DOCX 21 kb)


Population aging is a global phenomenon, and choosing appropriate medical care for the elderly is critical. Polypharmacy is suspected to increase the risk of adverse events (AEs) in older patients. We examined the AE profiles associated with polypharmacy and aging using the Japanese Adverse Drug Event Report (JADER) database. We attempted to mitigate the effect of patient-related factors using a multiple-logistic regression technique and data subsetting. We selected case reports for AEs as specified in the Medical Dictionary for Regulatory Activities (MedDRA). The association between polypharmacy and “renal disorder” or “hepatic disorder” was evaluated using reporting odds ratio (ROR) and adjusted for covariates using multiple-logistic regression. For renal disorder, advanced polypharmacy showed higher adjusted RORs, because the value of administered drugs group  was higher than that of the number of administered drugs group . The lower limit of the 95% confidence interval (CI) of adjusted ROR for age (≥ 60 years) was > 1 for renal disorder. For hepatic disorder, the adjusted RORs were as follows: 1.17 (1.14–1.20) for the number of administered drugs group (5–9) and 1.14 (1.11–1.18) for the number of administered drugs group (≥ 10). The adjusted RORs of hepatic disorder compared to those of renal disorder had lower adjusted RORs related to the increase in the number of administered drugs. Therefore, elderly individuals should be closely monitored for the occurrence of renal disorder when they are subjected to polypharmacy. This approach might apply to the simultaneous evaluation of the AE risk of polypharmacy and aging. Aging of the population is a global phenomenon, and appropriate medical care for the elderly should be ensured –6. A surThe elderly often have multiple diseases and, therefore, are administered numerous medications, which is referred to as polypharmacy –11. In tA standard definition has not been determined for polypharmacy, which could be appropriate or problematic. When medicine use is optimized, prescriptions are based on the best evidence, which improves the patient’s life expectancy and quality of life (QOL), and is considered appropriate polypharmacy. However, when the risk of interactions and adverse events (AEs) of multiple drug cotreatments affects patients’ QOL and compliance, this is considered problematic polypharmacy . DespiteIncreased risk of AEs in the elderly might cause physiological changes related to aging that alter pharmacokinetic and pharmacodynamic drug responses . In partThe spontaneous reporting system (SRS) of the real-world setting has been used in pharmacovigilance assessments –31. The The effect of aging and polypharmacy, such as the number of administered drugs, on AEs was evaluated separately in the previous studies ,17,24,33www.pmda.go.jp). All data of the JADER database were fully anonymized by the regulatory authority before we accessed them. The structure of the JADER database complies with the international safety reporting guidelines . The database consists of four data tables: 1) patient demographic information (DEMO), 2) drug information (DRUG), 3) AEs (REAC), and 4) primary illness (HIST). The JADER database does not contain the codes for identifying case reports (A1.11) and, therefore, we could not exclude duplicate cases from the same patient (https://www.pmda.go.jp/files/000145474.pdf). In the DRUG table, the causality of each drug was assigned a code according to its association with the AEs such as a “suspected drug,” “concomitant drug,” or “interacting drug.” All drugs in the “suspected drug,” “concomitant drug,” and “interacting drug” association classes were used for the analyses.Data from the JADER database, which contains data recorded from April 2004 to June 2017, were obtained from the PMDA website  have accepted the chronological age of 65 years as a definition of “elderly” or older person. For the calculation of the adjusted ROR, the reports were stratified by age as follows: ≤ 59 and ≥ 60 years, because 65 years was categorized into precise 10-year intervals in the JADER database. Neonate, baby, infant, child, young adult, first trimester, second trimester, and third trimester were categorized into the ≤ 59-year-old group. Elderly was categorized into the ≥ 60-year-old group. We excluded the adult and unknown items because these descriptions could not be precisely categorized into the ≤ 59 and ≥ 60-year-old groups. The number of administered drugs was categorized as < 5, 5–9, and ≥ 10 drugs ,22–25.www.meddra.org). The standardized MedDRA Queries (SMQ) index is widely accepted and used in analyzing the JADER database. We used the SMQ version 20.0 to extract case reports related to hepatic and renal disorders. We used the following SMQ for Drug related hepatic disorders—comprehensive search, acute renal failure, chronic kidney disease: SMQ codes: 20000006, 20000003, and 20000213, respectively   of the ≥ 5 drugs administered population stratified by age of ≤ 19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, ≥ 90 years was 28.7% , 32.2% , 34.3% , 37.7% , 42.1% , 44.8% , 46.5% , 47.3% , and 44.9% , respectively. The reporting rate of the population administered more than five drugs, which is commonly referred to as polypharmacy, increased with age and was the lowest and highest in the ≤ 19- and 80–89-year-old groups (28.7 and 47.3%), respectively .For the entire dataset, the number of administered drugs was 5.04 ± 4.66 (mean ± standard deviation) and was the highest and lowest in the 70–79- and ≤ 19-year-old groups (5.42 ± 4.79 and 3.93 ± 4.47), respectively. For hepatic disorder, the number of administered drugs was 5.37 ± 4.78 and was the highest and lowest in the 80–89 and 30–39-year-old groups (6.18 ± 4.85 and 4.52 ± 4.20), respectively. For renal disorder, the number of administered drugs was 6.12 ± 5.52 and was the highest and lowest in the 70–79- and ≤ 19-year-old group (6.44 ± 5.35 and 4.90 ± 6.09), respectively. The mean number of administered drugs for renal disorder was higher than that for hepatic disorder.After excluding incomplete reports that lacked information on the reporting year, age, or sex, 432,337 reports were included in the multiple logistic regression analysis. Using a stepwise logistic regression model, we selected significant variables related to AEs among the reporting year, sex, age, and the number of administered drugs, and examined the interaction between sex, age and the number of administered drugs .For hepatic disorder, the result of the final model indicated that in addition to the significant contribution of reporting year (p < 0.0001), sex , age , the number of administered drugs  and the number of administered drugs  were observed. The interaction between sex  and the number of administered drugs (≥ 10) was also significant (p = 0.0074). The adjusted RORs for the number of administered drugs  and the number of administered drugs (≥ 10) were 1.17 (1.14–1.20) and 1.14 (1.11–1.18), respectively. However, the interaction between age (≥ 60 years) and the number of administered drugs (5–9) or the number of administered drugs (≥ 10) was not significant (data are not shown).For renal disorder, significant contributions of reporting year (p < 0.0001), sex , age , the number of administered drugs , and the number of administered drugs  were observed. The interaction between sex  and age (≥ 60 years) was also significant (p < 0.0001). The adjusted RORs of age (≥ 60 years), administered drugs (5–9), the number of administered drugs (≥ 10), and interaction between sex  and age (≥ 60 years) were 1.32 (1.28–1.35), 1.27 (1.24–1.31), 1.82 (1.76–1.88), and 1.60 (1.59–1.69), respectively. The interaction between age (≥ 60 years) and administered drugs (5–9) or administered drugs (≥ 10) was not significant (data not shown).We also expressed the results in the heat map of the adjusted RORs obtained from the interaction terms (A*N) from the number of administered drugs and the stratified age groups based on 10-year intervals ] of administered drugs (≥ 10) group was higher than that [1.27 (1.24–1.31)] of the number of administered drugs (5–9) group . Drug inFor renal disorder, the lower limit of the 95% CI of adjusted ROR [1.32 (1.28–1.35)] for age (≥ 60 years) was > 1. In the literature, the effect of aging in renal disorders is clear . After tObjections will no doubt be raised that the interaction between aging and polypharmacy in renal disorder was not statistically significant. However, we constructed the visual heat map of adjusted RORs for AEs from the number of administered drugs and the stratified age group . For renFor hepatic disorder, polypharmacy showed the following slightly higher adjusted RORs: 1.17 (1.14–1.20) of administered drugs (5–9) group, 1.14 (1.11–1.18) of number of administered drugs (≥ 10) group . The adjThe heat map of adjusted RORs obtained from the interaction terms (A*N) of the number of administered drugs and the stratified age group based on 10-year intervals, revealed a negative correlation between the number of administered drugs and AEs for hepatic disorders . We do nMedia attention and publicity resulting from advertising or regulatory actions such as safety information from the PMDA might influence the JADER database reporting based on the year of reporting ,43. In tPolypharmacy is related to increased risk of being administered potentially inappropriate medications (PIM) ,45. BothThe analysis using SRS such as the JADER database has several limitations that are worth noting. The SRS is subject to over-reporting, under-reporting, missing data, exclusion of healthy individuals, lack of a denominator, and the presence of confounding factors . In genehepatic or renal disorders might be useful for evaluating aging-, polypharmacy-, or aging-polypharmacy-related AE associations in the interpretation of results, because the subsets consist of a population of patients that share a set of commonly administered drugs, risk factors, or diseases. This approach might be applicable for the simultaneous AE risk evaluation of polypharmacy and aging. We considered that the subset analyses focusing on drugs of interest, such as antibiotics that are expected to cause renal disorder, might be valuable. We expect our study to make a valuable contribution to the information available for clinicians and would facilitate the improvement of polypharmacy management.An observation could be made that the polypharmacy- and aging-related risks are different for each AE and drug. However, data subsetting using the SMQs such as renal disorder than hepatic disorder because the adjusted RORs in renal disorder were higher for those who were on polypharmacy than for those who were not. Age-related increase in renal disorder is well-known. Thus, older patients on polypharmacy treatments should be closely monitored for renal disorder. Our results are useful for improving the management of polypharmacy. Further research is needed to determine the specific associations between polypharmacy and AEs for each drug.To the best of our knowledge, this study was the first to evaluate the AE profiles related to aging and polypharmacy using the JADER database. We demonstrated that polypharmacy might be more closely associated with an increased risk of S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Tableodds) = β0 + β1Y + β2S + β3A + β4N + β5S * A + β6S * N + β7A * N. Reports were stratified by age as follows: ≤ 19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and ≥ 90 years. Adjusted RORs were calculated using male, 19-year-old ≤ group, and one drug as a reference group.log((XLSX)Click here for additional data file.S5 Tableodds) = β0 + β1Y + β2S + β3A + β4N + β5S * A + β6S * N + β7A * N. Reports were stratified by age as follows: ≤ 19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and ≥ 90 years. Adjusted RORs were calculated using male, 19-year-old ≤ group, and one drug as a reference group.log((XLSX)Click here for additional data file.S1 Figodds) = β0 + β1Y + β2S + β3A + β4N + β5S * A + β6S * N + β7A * N. Reports were stratified by age as follows: ≤ 19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and ≥ 90 years. Adjusted RORs were calculated using male, 19-year-old ≤ group, and one drug as a reference group.A) Number of the administered drugs, B) Age, C) Number of administered drugs * Sex , D) Age * Sex , E) Number of administered drugs * Age. log((TIF)Click here for additional data file.S2 Figodds) = β0 + β1Y + β2S + β3A + β4N + β5S * A + β6S * N + β7A * N. Reports were stratified by age as follows: ≤ 19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and ≥ 90 years. Adjusted RORs were calculated using male, 19-year-old ≤ group, and one drug as a reference group.A) Number of the administered drugs, B) Age, C) Number of administered drugs * Sex , D) Age * Sex , E) Number of administered drugs * Age. log((TIF)Click here for additional data file.


Iris pseudacorus (CWI) or Typha orientalis Presl. (CWT) and several unplanted ones (CWC) were set up and fed with secondary effluent of sewage treatment plant during the winter in Eastern China. The 16S rDNA Illumina Miseq sequencing analysis indicated the positive effects of I. pseudacorus on the bacterial community richness and diversity in the substrate. Moreover, the community compositions of the bacteria involved with denitrification presented a significant difference in the three systems. Additionally, higher relative abundances of nitrifying bacteria  were recorded in CWI compared with CWT  and CWC . Meanwhile, the average removal rates of NH4+-N and TN in CWI showed a prominent advantage compared to CWC, but no distinct advantage was found in CWT. The hardy plant I. pseudacorus, which still had active root oxygen release in cold temperatures, positively affected the abundance of nitrifying bacteria in the substrate, and accordingly was supposed to contribute to a comparatively high nitrogen removal efficiency of the system during the winter.The plants effect in subsurface flow constructed wetlands (SSF-CWs) is controversial, especially at low temperatures. Consequently, several SSF-CWs planted with  During the past several decades, constructed wetlands (CWs), which are an economical and environmentally friendly wastewater treatment technology, have been widely used for removing pollutants from a variety of wastewaters including domestic wastewater, agricultural wastewater, industrial wastewater, acid mine drainage, etc. around the world1234367β-proteobacteria and γ-proteobacteria, determined the rate-limiting step for nitrification that converts ammonium to nitrite with oxygen as the final electron acceptor, while a wider variety of bacterial phylogenetic groups contributed to the denitrification process8Microorganisms have been universally realized as an original and critical factor in the wetland purification process89Plants have several properties that make them an essential component in wetlands, but their exact effect is complex and controversial. Apart from absorbing and storing the nutrients from wastewater, the presence of macrophytes is believed to have a close relationship with the increased microbial density, activity and diversity in wetlands67121613615171819172021Iris pseudacorus) and oriental cattail  were chosen. The two different species have different physiological properties and growth characteristics; for example, the yellow flag can remain active, but the acrial part of the oriental cattail will wither away in the winter. Furthermore, to investigate the microorganism community in the substrate, the V3-V4 regions of the bacteria 16S rDNA were sequenced via an Illumina MiSeq 2500 platform. Because of its higher integrity and broader range of applications232526272930313233In the present research, yellow flag , TN (47.50%) and COD (81.07%) and was superior to the control, which had removal rates of 72.25% (NH4+-N), 39.33% (TN) and 73.57% (COD), respectively. For CWT, only NH4+-N removal (75.75%) presented a slight advantage compared to the control, while the removal rates of TN (34.17%) and COD (71.43%) were the lowest among the three systems. The removal efficiency of NO3−-N in the three systems was consistent at approximately 33.00%. NO2−-N was not detected in the effluent.The influent characteristics and the time for water sample determination were described in the second section . The overall performance of the two types of SSF-CWs is shown in I. pseudacorus shoot increased slightly from 226.75 gDW·m−2 to 249.35 gDW·m−2, although no significant difference was observed. Conversely, the biomass of the T. orientalis Presl. shoot declined sharply from 135.24 gDW·m−2 to 50.91 gDW·m−2 for the dormancy of aboveground part. However, for the roots biomasses, both of species rose indistinctively. The I. pseudacorus shoot and T. orientalis Presl. shoot increased from 633.47 gDW·m−2 to 681.83 gDW·m−2 and 552.31 gDW·m−2 to 562.86 gDW·m−2, respectively. With the change in the plant biomasses, the nitrogen accumulation in the plants changed accordingly. An increase of 0.42 g·m−2, 0.38 g·m−2 and 0.06 g·m−2 and a decrease of 0.46 g·m−2 were recorded for the I. pseudacorus shoot, I. pseudacorus root, T. orientalis Presl. root and T. orientalis Presl. shoot, respectively. The rate of root radial oxygen loss (ROL) and the root vitality in the two plants were also measured  and Verrucomicrobia (4.23%). In CWT, Proteobacteria and Bacteroidetes accounted for 72.73% and 22.59%, respectively. In the control, Proteobacteria (64.29%) and Bacteroidetes (27.26%) constituted the primary phyla, and Cyanobacteria (3.39%) and Verrucomicrobia (2.55%) followed.In addition to the richness and diversity of the microbial communities, the community composition in the substrate also plays a critical role in the pollutants removal in CWs. The abundance of different phyla of bacteria in the three types of SSF-CWs is shown in I, CWT and CWC, respectively. The order of the primary classes was β-proteobacteria (26.77%) > Flavobacteria (14.13%) > γ-proteobacteria (13.57%) > Sphingobacteria (5.55%) > Chloroplast (4.93%) > δ-proteobacteria (4.48%) > α-proteobacteria (2.30%) in CWI. In CWT, it was β-proteobacteria (46.26%) > γ-proteobacteria (21.39%) > Flavobacteria (16.35%) > Sphingobacteria (2.12%) > α-proteobacteria (1.88%) > δ-proteobacteria (1.07%). In CWC, they mainly included γ-proteobacteria (35.72%), β-proteobacteria (18.51%), Flavobacteria (15.58%), Sphingobacteria (4.95%), δ-proteobacteria (3.91%), α-proteobacteria (2.68%) and ε-proteobacteria (2.18%).The bacterial composition of the three systems at the class level is shown in Nitrosomonas, Nitrosospira and Nitrospira) in the three systems are shown in β-proteobacteria (17 genera) and γ-proteobacteria (10 genera). The bacterial community composition showed a significant difference at the genera level among the three systems. In CWI, Flavobacterium (18.28%), Albidiferax (10.02%) and Ohtaekwangia (7.72%) constituted the three dominant genera. In CWT, they were Deefgea (22.21%), Flavobacterium (19.47%), Albidiferax (14.08%) and Halomonas (12.21%). In CWC, the two dominant genera composed of Halomonas (27.81%) and Flavobacterium (17.62%). Among the 40 listed genera, there were at least 8 genera reported to involve in denitrification. They included Azospira, Dechloromonas, Aeromonas, Shewanella, Halomonas, Pseudomonas, Arcobacter and Flavobacterium. The compositions of these denitrifying bacteria were also different among the three systems. In CWI, the order of their relative abundances was Flavobacterium (18.28%) > Halomonas (2.91%) > Aeromonas (1.81%) > Pseudomonas (1.68%) > Dechloromonas (1.29%) > Azospira (0.67%) > Shewanella (0.56%) > Arcobacter (0.24%). In CWT, there were richer Flavobacterium (19.47%) and Halomonas (12.21%). In CWC, the dominant denitrifying bacteria were Halomonas (27.81%) and Flavobacterium (17.62%), followed by Aeromonas (3.22%) and Arcobacter (2.99%). In addition, Nitrosomonas, Nitrosospira and Nitrospira were also observed in the three systems, although their relative abundances were not rich. CWI presented the highest proportion of nitrifying bacteria, and the relative abundances were 0.4140% (Nitrosomonas), 0.2402% (Nitrosospira) and 0.4318% (Nitrospira), respectively. The lowest percentage of the three nitrifiers was observed in CWT, which recorded as 0.2074% (Nitrosomonas), 0.0648% (Nitrosospira) and 0.0181% (Nitrospira), respectively. In CWC, their abundances were 0.3013% (Nitrosomonas), 0.1107% (Nitrosospira) and 0.1185% (Nitrospira), respectively.The primary genera (relative abundance >0.50%) with the addition of three nitrifying bacteria , which converted ammonium to nitrite, and the nitrite-oxidizing bacteria (NOB), which converted nitrite to nitrateI. pseudacorus remained active, but the acrial part of T. orientalis withered away. The rate of root radial oxygen loss (ROL) was also higher in CWI compared to CWT. This might contribute to a higher richness of aerobic microorganism in CWI. Moreover, the carbon source supply is also an important factor that influences the microbial communities in the substrate6T. orientalis, a dormant plant during the winter, showed a decline in shoot biomass because of the senescence of leaves during the determination period. Chen et al.3857T. orientalis system. This could be because quite a large proportion of plant litter, such as highly crystalline lignocellulose, was recalcitrant and the complete degradation of litter needed a relatively long time7The diversity of the microbial composition suggested a diversity of the functional characteristics that were relevant to pollutant removal in wastewater162238524054I. pseudacorus have more active effects towards the microbial community compositions in the substrate. Moreover, the root activity and growth status of the macrophytes in CWs also influence the richness and enzyme activity of microbial populations1052et al.Brocadiales in the phylum Planctomycetes, has not been observed in this study. Besides, the coexistence of some autotrophic bacteria such as Cyanobacteria in wetlands was also reported benefit to the pollutants degradation, because they could provide the heterotrophic bacteria with oxygen, which played a similar and complementary role in addition to the macrophytes to a certain degree2264In addition, some other factors may also have significant impact on the contaminants removal in CWs. Plant root exudates can also be used as the dissolved organic matter for the metabolism of heterotrophic bacteriaI. pseudacorus had a positive effect on the bacterial abundance and eventually on the community composition in the substrate. The presence of a hardy plant with active root oxygen release enhanced the relative abundance of nitrifying bacteria in the substrate, and consequently was supposed to contribute to the increase in the nitrogen removal efficiency of the system during the winter. However, some other factors, such as the bacteria population and activity, the presence of Cyanobacteria and ammonia-oxidizing archaea may also have an important influence on the nitrogen removal in SSF-CWs. Hence, further researches will be needed to understand detailed mechanisms referring to the biodegradation process of pollutants in CWs.In conclusion, 16S rDNA Illumina Miseq sequencing revealed detailed information about the interaction between the plant and the bacterial community composition in SSF-CWs. The hardy plant I. pseudacorus, four were planted with T. orientalis Presl., both at an initial density of 30 plants per m2, and four controls without plants were built in Huai’an, Jiangsu province, China . The dimensions of each mesocosm were (0.8 m)2 × π × 0.75 m, and each bed consisted of three layers: 100 mm deep rough sand (1–2 mm in diameter) on the top, 100 mm deep gravel  in the middle, and 550 mm deep gravel  at the bottom (To simulate subsurface flow constructed wetlands (SSF-CWs), four outdoor mesocosms were planted with e bottom .th, 2014 to Sept 10th, 2014) was for plants growth. The mesocosms were fed continuously with natural water until the plant shoots grew long enough. (2) Stage II  was for microbial establishment. The mesocosms were fed continuously with wastewater with appropriate artificial modification on the base of a secondary effluent from a sewage treatment plant for domestic wastewater. The pH of the influent water was 7.12–7.88, the average hydraulic loading rate (HLR) was 187.5 mm·d−1 and the hydraulic retention time (HRT) was 4 days. The concentrations of dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH4+-N), nitrite nitrogen (NO2−-N), nitrate nitrogen (NO3−-N) and total nitrogen (TN) in the influent water were 9.2–9.8 mg·L−1, 55.0–65.0 mg·L−1, 9.5–12.5 mg·L−1, 0.3–0.6 mg·L−1, 4.5–5.5 mg·L−1 and 18.0–24.0 mg·L−1, respectively. (3) Stage III  was for variables determination. The operation scheme was the same as Stage II. The water temperature was 4.3–6.7 °C during Stage III.The operation of the SSF-CWs was divided into three stages. (1) Stage I . The concentrations of NH4+-N, NO2−-N, NO3−-N, TN and COD were determined with a Water Quality Analyzing System . All variables were analysed according to standard analytical proceduresThe water temperature in the mesocosms was recorded by the Temperature and Illuminance Data Logger . The pH was determined by a portable Multi-parameter Water Quality Meter . Dissolved oxygen (DO) was monitored Plant samples were harvested and separated into roots and shoots, dried at 65 °C to a constant weight, and then ground into powder. The N content was determined by an Elemental analyzer  at the beginning and end of the experiment68nd 2015. Then, the samples in each system were mixed well and vigorously shaken at 200 rpm for 3 h in sterile glass bottles to isolate the biofilm from the substrate surface. After centrifuging twice , the precipitate was collected for subsequent DNA extraction, 16S rDNA PCR amplification and Illumina MiSeq sequencing.As shown in −1 in sterile water. The universal primer sets 341F (5′-CCTAYGGGRBGCASCAG-3′) and 785R (5′-GACTACHVGGGTATCTAATCC-3′) were used for amplification of the V3-V4 regions of 16S rDNA. A 10 ng template, 0.5 μL of forward primers and 0.5 μL of reverse primers were added into a 25 μL reaction system for the PCR reaction. Thermal cycling consisted of denaturation at 94 °C for 3 min, which was followed by 30 cycles of 94 °C for 10 s, 55 °C for 15 s and 72 °C for 30 s, and finally held at 72 °C for 7 min. AmpureBeads  were used for the PCR product purification.DNA was extracted from the samples using a QIAamp Fast DNA Stool Mini Kit  according to the manufacturer’s instructions. DNA yields were determined using a SpectraMax 190 , and the integrity was evaluated via 1.0% agarose gel electrophoresis. Then, DNA was diluted to 1 ng·μLThe sequencing libraries were constructed with an NEB Next Ultra DNA Library Prep Kit for Illumina , and then a Qubit 2.0 Fluorometer  was used to assess the libraries quality. Then, the libraries were sequenced on an Illumina MiSeq 2500 platform.The FASTX-Toolkit (version 0.0.14) and Mothur program (version 1.34.0) were used for the analysis, merging and quality filtering of the raw data sequences. All of the reads were quality filtered using an average quality value of 20 (Q20) during demultiplexing. Short reads (length < 40 bp) and chimeras were excluded. Reads were clustered by degree similarity levels with the UCLUST program (version 1.2.22q). Sequences with ≥ 97% similarity were assigned to the same genus. A RDP classifier (version 2.2) was used to annotate the taxonomic information. The Mothur program was used for alpha diversity analysis. ACE estimated the richness of the species, while the Shannon index estimated the diversity of the species.All statistical tests were performed with the statistical program SPSS 17.0 . The data were analysed with one-way analysis of variance to compare the performance of each mesocosm. Duncan’s test was performed to detect the statistical significance of differences (p > 0.05) between the mean values of the treatments.How to cite this article: Wang, P. et al. A Hardy Plant Facilitates Nitrogen Removal via Microbial Communities in Subsurface Flow Constructed Wetlands in Winter. Sci. Rep. 6, 33600; doi: 10.1038/srep33600 (2016).


Weissella cibaria 64 and Leuconostoc mesenteroides 12b, able to increase antioxidant activity, were specifically studied as potential starters for lactic fermented pineapple juice.Tropical fruits like pineapple, papaya, mango, and beverages such as green or black teas, represent an underestimated source of antioxidants that could exert health-promoting properties. Most food processing technologies applied to fruit beverages or teas result in an impairment of inherent nutritional properties. Conversely, we hypothesise that lactic acid fermentation may constitute a promising route to maintain and even improve the nutritional qualities of processed fruits. Using specific growth media, lactic acid bacteria were selected from the fruit phyllosphere diversity and fruit juice, with the latter undergoing acidification kinetics analyses and characterised for exopolysaccharide production. Strains able to ferment tropical fruit juices or teas into pleasant beverages, within a short time, were of particular interest. Strains  One of the objectives of food research is to investigate new food products that promote health. During the last decade, consumers have increasingly believed that food contributes directly and effectively to health promotion. In this context it has been widely suggested that high intake of fruits and vegetables, nutrient-rich plants and antioxidant-rich beverages like tea and coffee may reduce the risk of degenerative and oxidative-stress related diseases ,2. FreshKimchi, a traditional fermented mix of vegetables and Kombucha, a fermented tea beverage, were associated with antimutagenic, antioxidative, antiaging, cholesterol-lowering activities and prevention against cancer and cardiovascular diseases [Lactobacillus plantarum, Pediococcus pentosaceus or Weissella cibaria [L. plantarum, Leuconostoc mesenteroides, P. pentosaceus, Lb. delbrueckii subsp. lactis, Bifidobacterium breve and B. thermophilum increases Vitamin C concentration, phenolic content and antioxidant activity of carrots, French beans, marrows, black beans, pomegranate juice, soy milk and cowpeas [Lactic acid fermentation is one of the oldest ways of food processing and preservation, liable to keep or increase the safety, nutritional, sensory and shelf-life properties of foods. Lactic acid bacteria (LAB) are a group of gram-positive bacteria, producing lactic acid as the main product of carbohydrate fermentation. During fermentation, LAB produce substances such as bacteriocins, exo-polysaccharides (EPS), aroma compounds, enzymes, B vitamins  or low-calorie polyols , which enhance the safety, nutritional quality, sensory properties or antioxidant activities of food . In thisdiseases ,9. Severdiseases ,11,12,13 cibaria ,14. Lact cowpeas ,16,17,18 cowpeas . Lactic In this study, we aimed to develop an attractive fermented beverage through the action of autochthonous lactic acid bacteria isolated from fruit and vegetables grown in Réunion Island. Tropical fruits like pineapple, papaya and mango, together with antioxidant-rich beverages like green and black teas, were chosen as substrates for lactic acid fermentation. The selected raw materials characterised by varying phytochemical composition, more particularly phenolic compounds, were subjected to lactic acid fermentation with the view to identifying pairs of lactic acid bacteria strains and raw material that result in fermented beverages which are technologically feasible, characterised by a pleasant sensory and endowed with increased antioxidant activity.Lycopersicon esculantum), papaya (Carica papaya) and sliced cabbage (Brassica oleacera var. capitata) grown on Réunion Island, were studied to produce a fermented beverage [16S-rRNA, pheS and recA gene sequencing. Lc. citreum DSM20188, W. confusa DSM20196, W. cibaria DSM14295, Lc. pseudomesenteroides DSM20193 and DSM5625, Lb. plantarum DSM2601 were used as reference strains. LAB were grown on MRS (Man Rogosa Sharpe) agar at 30 °C for 72 h. For strain reactivation, one or two colonies or a loop of −80 °C stock were suspended in 9 mL of MRS broth and incubated at 30 °C for 48 h. Thereafter, cultures were homogenised by vortex and 0.2 mL of this suspension was used to inoculate 9 mL of MRS broth. Cell population was estimated by measuring absorbance at 660 nm wavelength.A total of 28 lactic acid bacteria, isolated from tomatoes  and Rubio et al., (2014) [−1 dNTP, 0.5 µmol·L−1 each primer, 1.5 mmol·L−1 MgCl2 and 0.04 U·µL−1Taq polymerase . The PCR reaction was performed in a 45 µL volume with 10 µL of DNA solution. The thermal program consisted of 5 min at 95 °C; 35 cycles of 1 min at 95 °C, 30 s at 56 °C, 1 min 15 s at 72 °C; 7 min at 72 °C. In order to differentiate Lb. plantarum/paraplantarum/pentosus, recA gene sequencing was performed according to Torriani et al., (2001) [−1 MgCl2, 0.25 µmol·L−1 ParaF, PentF and PREV, 0.12 µmol·L−1 PlanF, 12 µmol·L−1 dNTP and 0.025 U·µL−1Taq polymerase. The thermal program consisted of 3 min at 94 °C; 30 cycles of 30 s at 94 °C, 10 s at 56 °C, 30 s at 72 °C; 5 min at 72 °C. The products of positive PCRs (a 318 bp amplicon for Lb. plantarum spp. or a 107 pb amplicon for Lb. paraplantarum spp.) were purified with a GelElute Extraction kit .DNA extraction was performed by using Instagen protocol . escribed . The amp, (2014) ,22 with , (2001) . MultiplPCR products were sequenced by the Sanger method with FD1m, pheS-21F, PlanF and ParaF primers as previously described .−1) agar as previously described [EPS production was determined using MRS sucrose  curves as previously described. Lag time (λ) was the time corresponding to an initial pH variation of less than 0.1. Minimal pH (pHmin) was the final constant pH value. Maximum acidification rate (VM), time to reach the maximum acidification rate without the lag phase (tM), and pH at maximal acidification rate (pHM) were determined from dpH/dt = f(t) curves.Kinetics of acidification was performed as previously described using MRS broth as culture media . Each cug for 5 min. The supernatant was removed and the cell pellet was washed twice with sterile distilled water and re-suspended in 9 mL of water. Then, 20 µL of this bacterial suspension were inoculated in 180 µL of clarified apple juice . This juice contained 19.4 g·L−1 glucose, 63.1 g·L−1 fructose and 9.7 g·L−1 sucrose. The control medium corresponds to apple juice plus 20 µL of distilled water. Microplates were incubated at 30 °C for 96 h. OD at 660 nm was measured every 2 h . Results were expressed as log (OD96/OD0). Apple juice culture was performed in triplicate for each strain.Growth in apple juice was performed in sterile 96-well microplates. LAB were grown for 48 h at 30 °C in 9 mL MRS broth. Bacterial suspension was centrifuged at 8000× −1 glucose, 41.8 g·L−1 fructose and 42.1 g·L−1 sucrose. Papayas or mangoes were washed and peeled. The seeds or stones were removed and mixed in order to obtain a purée. The latter was pasteurised for 5 min at 75 °C using a water bath with temperature monitoring and cooled to room temperature before fermentation. For the tea infusion, sugar cane syrup was added to distilled water in order to reach 10% sucrose concentration. One tea bag was infused for 5 min in 250 mL of boiling sweetened water and the infusion was cooled at room temperature before fermentation.Commercial pineapple juice was used directly for all experiments. Pineapple juice contained 41.1 g·Lg for 5 min, the cell pellet was washed twice with sterile distilled water and re-suspended in 20 mL of sterile water in order to have a concentrated cell culture. Optical density at 660 nm was measured and volume of inoculation was calculated in order to have an initial OD of 0.05. With the appropriate volume, 40 mL of pineapple juice or green tea or black tea or 40 g of papaya or mango purée were inoculated with the concentrated culture. Substrates were incubated at 30 °C without agitation. Thereafter, fermented products were incubated at −20 °C for 15 min to stop the fermentation. Fermentation was performed in triplicate for each strain.LAB strains were reactivated for 48 h at 30 °C in 9 mL of MRS broth. Then, 48 h reactivated cultures were used to inoculate 50 mL pre-culture of MRS broth with an initial optical density at 660 nm of 0.05. Pre-cultures were incubated at 30 °C for 48 h. After centrifugation at 8000× In order to determine optimal time for fermentation and select a reduced number of potential starters, pineapple juice was subjected to fermentation at 30 °C for two days or four days with each of the 35 strains and placed at 4 °C for 24 h before sensory analysis. A qualitative sensory analysis was carried out by five non-trained people. Substrates were scored from “excellent” to “unsatisfactory” for colour, aroma, appearance and uniformity. Strains were scored as (++) if global sensory analysis was excellent, (+) for a good global sensory analysis and (−) for an unsatisfactory global sensory analysis.W. cibaria 64 and Lc. pseudomesenteroides 12b were placed at 4 °C for 16 days. After inoculation, following fermentation or periodically over storage, 1 mL of non-started and started juices were suspended into 9 mL sterile peptone water solution and homogenised. Lactic acid bacteria population was determined on MRS agar at 30 °C for 48–72 h. Yeast counts were determined on glucose chloramphenicol agar at 25 °C for 5 days. Total mesophilic bacteria counts were determined on nutritive agar at 30 °C for 72 h. Enterobacteria population was calculated from colonies observation on VRBG agar after 48 h at 37 °C. The pH, sugar concentration and antioxidant activity were also monitored during the same period.After 48 h of fermentation, control juice and started pineapple juice with −1 flow rate at 80 °C with a Ca USP L19 (250 × 4.0 mm) column . Calibration curves were prepared from sucrose, glucose and fructose standards and were used for the quantification. The concentration range for each standard was 0.5 g·L−1 to 0.03125 g·L−1. The correlation coefficient r² for the calibration curves were 0.9987, 0.9993 and 0.9860 for sucrose, glucose and fructose, respectively. Pineapple and apple juice were 100-fold diluted for HPLC analysis.The Miller method was used to determine reducing sugar concentration , with glW. cibaria 64 or Lc. pseudomesenteroides 12b were refrigerated, encoded with three-digit random numbers and served (20 mL). Products were served anonymously with randomisation, and the analysis was performed in triplicate. Control and fermented juices were scored from 0 to 10 for colour, odour, texture, flavour, taste and general quality. The sensory attributes used for this study were: yellow, green, and orange for the colour; pineapple, soft, acid, fermented, fruity, yoghurt and sweet for the odour; smoothness, fluidity, watery and syrupy for texture; acid, sweet, bitter and salt for the flavour; fresh, pineapple, fruity, yoghurt, spicy, astringent, hot, fermented, sweet-sour, and sparkling for the taste.Sensory profile was carried out by a panel of nine trained members. After fermentation, non-started pineapple juice and fermented juice started with the strains W. cibaria 64 or Lc. pseudomesenteroides 12b were also evaluated by 37 untrained panellists (17–50 years old). The panel was composed of 59.5% women and 40.5% men. Most of the panellists were under 20 years old or 20–30 years old. Most of the panellists are used to drinking pineapple juice at least once per month or not at all . The two fermented pineapple juices were placed at 4 °C for two days before hedonic analysis. The samples were identified with random three-digit codes, distributed in portions of 20 mL into transparent cups and served in a randomised order. The panellists were asked to evaluate the overall acceptability, colour, odour, taste, pineapple taste and texture. Scales from 1 (dislike extremely) to 10 (like extremely) were used for overall acceptability, odour and taste. Scales from −2  to 2  were used to evaluate the colour, pineapple taste and texture, respectively.The fermented juice started with strains g for 20 min at 4 °C. Results are expressed in grams of gallic acid equivalent (GAE) per litre for pineapple juice, green and black tea. For mango and papaya purée, results were expressed in milligrams of GAE per 100 g of puree.Determination of TPC was performed using the Folin–Ciocalteu method with gallic acid as standard as previously described . PineappDPPH radical scavenging activity was performed as previously described  with somRed blood cell haemolysis assay was performed as previously described . Blood sg for 5 min. For each preparation, at least 30 mL of pooled plasma was used as starting material. Plasma density was adjusted to 1.22 g·mL−1 by adding solid KBr. Three millilitres of KBr-loaded plasma was layered in the bottom of a centrifuge tube . The loaded plasma fraction was gently overlaid with five millilitres of a KBr solution (density 1.063 g·mL−1). Finally, the KBr solution was overlaid with one millilitre of milliQ water. LDLs isolation was performed by ultracentrifugation  in a Beckman Coulter centrifuge equipped with a SW 41 Ti rotor. This step resulted in five layers from top to bottom: Residual water solution; Low-density lipoprotein fraction (density lower than 1.063 g·mL−1); Residual KBr solution; High-density lipoprotein fraction; Residual plasma solution. LDL was gently collected and dialyzed against PBS 1× (pH 7.4). LDLs were assayed for protein concentration by the Bradford method using Bovine Serum Albumin (BSA) as the standard. LDLs were sterile filtered and stored at 4 °C in the dark for no longer than 15 days. LDL oxidation was determined by measuring conjugated diene formation at 234 nm, as previously described [−1. Pineapple juices and papaya pulps were 500-fold and 50-fold diluted, respectively. Results are expressed in half-time oxidation (V-50) in min, obtained from GraphPad Prism 5.01 with a Blotzman sigmoidal nonlinear regression, as previously described.Blood samples were obtained from normolipidemic healthy volunteers. Red blood cells and plasma were separated by centrifugation at 2000× p-value < 0.0001). Significant differences versus a control or by pairs were tested with Dunnett’s and Ryan, Einot, Gabriel, Welch q (REGWQ) tests respectively.The software XLSTAT  was used for all statistical analyses. Significant effects of factors were detected with a Fisher test . Pairwise comparison showed that three strains, Lc. pseudomesenteroides 89, Lc. mesenteroides 1 and Lc. mesenteroides 28 exhibited the highest growth yield. Other strains with significant growth ranked from the highest to the lowest growth yield were Lc. pseudomesenteroides 39, Lc. lactis 24, Lc. mesenteroides 6a, Lc. pseudomesenteroides 27b, W. confusa 38, W. confusa 59, Lc. pseudomesenteroides 60, Lb. plantarum 17a, W. cibaria 21, Lc. pseudomesenteroides 56, Lc. citreum 2, Fb. tropaeoli 77 and W. paramesenteroides 37, in addition to three reference strains, Lc. citreum DSM20188, W. confusa DSM10196 and W. cibaria DSM14295 . The same test applied to RSA, showed increases for fermented papaya started with DSM10196, 6a, 39, 5, 17, 28, 1, 56, 10b and 12b with p-values raking from 0.001 to 0.05, respectively. In fermented papaya pulp, TPC ranged between 32.8 and 64.8 mg GAE/100g of pulp. RSA was in the extreme range of 2.9%–57.9% with a mean at 38.6%. These values were notably below the ones observed for tea. The phenolic level detected for papaya was close to that reported by Septembre-Malaterre et al., (2016) [Fermentation of papaya pulp resulted for several starter strains in changes in TPC and in RSA, compared to the control conditions B. Dunnet, (2016) : 33.4 an−1 and a RSA of 57.3% ± 1.3%, which is consistent with previous data [W. cibaria 64  . With this assay, other significant increases of activity were detected for juice started with Lc. pseudomesenteroides DSM10193  and Lc. pseudomesenteroides 56 .Non-fermented pineapple juice exhibited a TPC of 0.45 ± 0.02 g GAE·Lous data . For pine 0.045) C. Fermenp-coumaric acid and sinapic acid) and in glucoside hydroxycinnamic derivatives [Lactobacillus spp [W. paramesenteroides, Lc. mesenteroides and Lc. fallax while feruloyl esterase was observed in Leuconostoc spp. [W. confusa, W. cibaria and Leuconostoc spp. may partly explain the observed changes but RSA modulation was not species-dependent.Papaya pulp and pineapple juice share several common features. They are rich in hydroxycinnamic acids . Besides this observation, the half-time was reduced for pulp started with 10b versus incubated pulp  and for juice started with 12b versus incubated juice. For LDL oxidation assay, half-time with control cells was significantly lower than with 500-fold-diluted incubated pineapple juice and juice started with 56, 12b or 64 . Other sensory attributes did not significantly differ between the control and the 64-started juice. Hot and sparkling tastes were more present in pineapple juice started with strain 12b compared to the control and to 64-started juice (p < 0.001). Moreover, 12b-started juice was characterised by a more pronounced sweet and sour taste compared to the control juice (p < 0.05). Lastly, the pineapple character was significantly lower in 12b started juice compared to control juice (p < 0.01). These characteristics result in a significantly reduced overall quality for 12b started juice compared to non-fermented juice (p < 0.05). Interestingly, PCA analysis showed a very clear grouping of assays according to a limited number of sensory attributes (W. cibaria 64-started juice properties, whereas Lc. pseudomesenteroides 12b-started juices were characterised by hot and sparkling tastes and non-fermented juice by its marked pineapple taste.Pineapple juice started with tributes . Yogurt Altogether, started juice was liked with global quality scores of 5.5 and 5.1, respectively, for juices started with 64 and 12b. The non-started juice was more appreciated, with a score of overall quality of 6.9.Hedonic test scores showed that fermented pineapple juice started with strain 12b was more appreciated than that started with strain 64, but the difference was not significant . Colour,A set of 34 strains from 13 species with highly diverse characteristics was screened for the ability to start lactic fermentation of juice, pulps and tea infusions and to lead to pleasant beverages with preserved antioxidant properties. Two strains were particularly investigated, 64 and 12b. The fermented juices were globally appreciated and exhibited peculiar sensory characteristics.W. cibaria 64 produced EPS, had moderate acidification ability and grew poorly in apple or pineapple juice. Pineapple juice fermented with this strain exhibited a significant increase in TPC and RSA, which was maintained during refrigerated storage. However, other antioxidant activity determination assays did not succeed in detecting changes. Lc. pseudomesenteroides 12b also produced EPS but exhibited a slow acidification profile. Its growth in apple juice was poor but good in pineapple juice. With this strain as starter, an increase in antioxidant activity was noted in fermented papaya pulp but remained unchanged in fermented pineapple juice. In a green tea infusion or mango pulp, fermentation with this strain resulted in decreased TPC. These differences probably reflect different enzymatic activities that modulate the composition of fruit juice over-fermentation. The investigation of metabolite changes in fruit juice during lactic fermentation with these strains and the determination of enzymatic activities involved in polyphenolic compounds’ hydrolysis would lead to a better understanding of the observed properties of fermented juices. Lastly, fruit lactic fermentation with strains from those species is an unusual event, though these species are often detected in fermented foods of plant origin [t origin ,49,50 ant origin ,52.


The supplementation of the allopathic treatment with resveratrol had a beneficial effect on all monitored parameters, which serve as major risk factors for stroke.Stroke is a leading cause of mortality worldwide, as well as a source of long-term disabilities and huge socioeconomic costs. This study investigates the effects of resveratrol, an antioxidant supplement, on blood pressure, weight status, glucose, and lipid profile in patients who had a stroke in the last 12 months. Two hundred and twenty-eight patients were divided into three groups: group I received only allopathic treatment (control group), while groups II and III received allopathic treatment with a daily supplementation of oral resveratrol  for 12 months. In all groups, the changes of the studied parameters were monitored at 6 and 12 months from the initial evaluation. In groups II and III, resveratrol induced significant changes ( Neurodegenerative, circulatory, and cardiovascular diseases and cancers are considered as direct consequence of the complex of phenomena, named “oxidative stress” –3. AmongDuring the pathophysiology of stroke, reactive oxygen species (ROS) are generated, which can trigger chain reactions that destroy the neuronal membranes , 7. TherReactive oxygen species have an important role in normal physiological processes, being also implicated in a lot of disease processes, where they mediate damage to cell structures, including membranes, lipids, deoxyribonucleic acid (DNA), and proteins. Oxidative stress has an important role in the pathogenesis of ischemic brain injury that follows a cerebrovascular attack, having as goal the cerebral vasculature. The primary reactive oxygen species (like superoxide) and its derivatives, in animal models with ischemic stroke, cause vasodilatation by opening the potassium channels, altering the vascular reactivity, and breaking down the blood-brain barrier .Diabetes mellitus and atherosclerosis are diseases that are associated with chronic inflammation produced by ROS , 16. ConTo neutralize these free radicals, the presence of potentially neutralizing agents (antioxidants) in the body is needed . ParticuNumerous studies have shown the effectiveness of resveratrol in improving health and preventing chronic diseases. However, it is unclear whether these effects persist with prolonged administration of resveratrol , 26. ThiBaile Felix Clinical Rehabilitation Hospital where they followed a complex medical physical rehabilitation program that imposed restrictions for BP values.This study included patients who had first stroke in the last 12 months, hospitalized for recovery treatment during 2011–2015 . All patients were clinically stabilized after stroke. When they were included in this study, the patients were hospitalized in the The research was conducted in accordance with the WMA Ethical Declaration of Helsinki and was approved by the Ethics Commission of the Council of Medicine and Pharmacy Faculty, University of Oradea, Romania. Each patient included in this study signed an informed consent form, before the inclusion.group I (control), (n = 92 patients) who underwent allopathic  treatment combined with medical physical rehabilitation, while groups II (n = 81 patients) and III (n = 55 patients) received allopathic treatment and medical physical rehabilitation, combined with a 12-month supplementation with an oral daily dose of resveratrol . The patients were distributed into groups using the method of selection for each group, depending on the patient's willingness of taking 100 or 200 mg resveratrol, according to every patient's history and clinical characteristics, so that there were no significant differences between groups. Allopathic treatment was individualized for each patient, depending on the clinical characteristics and associated pathology. All patients were evaluated initially, at 6 months and at 12 months from the beginning of the treatment. The following parameters were monitored: BP, weight status, lipid profile  cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides), and glucose profile.Two hundred and twenty-eight patients were divided into three groups: Blood pressure measurement was performed following the 2007 Guidelines for the Management of Arterial Hypertension .2) [To determine the patients' weight status, the BMI was calculated using the following equation  .Blood samples were obtained in the morning after a 12–14-hour meal break and were used to evaluate the levels of total cholesterol, triglycerides, HDL and LDL cholesterol, basal glucose, and glycosylated hemoglobin (HbA1c).The cholesterol, triglycerides, HDL cholesterol, and LDL cholesterol assays were performed on the Beckman Coulter AU680 analyser using Beckman Coulter reagents, produced by Beckman Coulter Inc., Ireland. The methods of determination included the oxidase-peroxidase method for cholesterol, enzymatic glycerol-3-phosphate oxidase method for triglycerides, and colorimetric direct method for HDL and LDL cholesterol.Basal glucose was determined by the hexokinase method, the values on an empty stomach being used for the selection of all subjects. A normal level of basal blood glucose was considered a plasmatic concentration of <100 mg/dL .Glycosylated hemoglobin was determined only for diabetic patients. It is a real image of glucose control over a period of 90–120 days retrospectively . DetermiThe internal quality control was performed according to ISO 15189: 2013 standard. Variation coefficients were as follows: cholesterol CV% = 2.05, triglyceride CV% = 1.95, HDL cholesterol CV% = 2.29, LDL cholesterol CV% = 1.74, glucose CV% = 2.05, and HbA1c CV% = 0.82.t test) and χ2 were calculated.Statistical analysis was done using EPIINFO, version 6.0, a program of the Center for Disease Control and Prevention from Atlanta, adapted to the medical statistics processing. Average parameter values, frequency ranges, standard deviations, and statistical significance tests by the Student method  was measured. Based on statistical literature, the interpretation of this index was compiled, namely, small ES = 0.20, medium ES = 0.50, and large ES = 0.80. When expressing the findings of a quantitative study, ES is important because a agnitude , 31.p > 0.05) between the three groups .In terms of demographic and clinical characteristics, there were no significant differences (e groups .p > 0.05) in the mean BP values between the three experimental groups  Systolic BP. In the group receiving 100 mg resveratrol, the systolic BP value decreased from 148.02 mmHg to 143.12 mmHg at 6 months (p < 0.05) and to 139.85 mmHg in the next 6 months (p < 0.05). In the group receiving 200 mg resveratrol, the systolic BP value decreased from 149.21 mmHg to 142.02 mmHg at 6 months (p < 0.05) and to 139.35 mmHg in the next 6 months (p < 0.05). In the control group, systolic BP values decreased from 148.42 mmHg to 146.10 mmHg at 6 months (p > 0.05) and to 145.32 mmHg in the next 6 months (p > 0.05).p < 0.05) and 0.65 in the group receiving 200 mg resveratrol (p < 0.05), while in the control group, ES was 0.20 (p > 0.05).Comparing data  at the i(2) Diastolic BP. In the group receiving 100 mg resveratrol, the diastolic BP value decreased from 88.29 mmHg to 85.89 mmHg at 6 months (p > 0.05) and to 84.27 mmHg in the next 6 months (p > 0.05). In the group receiving 200 mg resveratrol, the diastolic BP value decreased from 88.47 mmHg to 85.91 mmHg at 6 months (p > 0.05) and 84.10 mmHg in the next 6 months (p > 0.05). In the control group, the diastolic BP value decreased from 87.61 mmHg to 86.39 mmHg at 6 months (p > 0.05) and 85.67 mmHg in the next 6 months (p > 0.05), respectively (ectively .p < 0.05), 0.39 in the group receiving 200 mg resveratrol (p < 0.05), and 0.17 for the control group without resveratrol supplementation (p > 0.05).Comparing data at the initial evaluation and at 12 months of treatment, ES was 0.37 in the group with 100 mg resveratrol (p > 0.05) in BMI values between the two experimental groups. After a 12-month administration of resveratrol at 100 and 200 mg, we recorded a significant decrease (p < 0.05) in the mean BMI values, compared to the control group , resulting in an ES = 0.35; in the group receiving 200 mg resveratrol, from 29.50 kg/m2 to 27.56 kg/m2 (p > 0.05), resulting in an ES = 0.45; in the nonresveratrol group, the BMI had a minimal decrease from 29.95 kg/m2 to 29.42 kg/m2 (p > 0.05), resulting in an ES = 0.12.The mean BMI values decreased after 6 months in the group receiving 100 mg resveratrol from 29.47 kg/m2 to 26.75 kg/m2 (p > 0.05), resulting in an ES = 0.27; in the group receiving 200 mg resveratrol, a decrease in BMI was noticed from 27.56 kg/m2 to 25.77 kg/m2 (p < 0.05), resulting in an ES = 0.42; in the group without resveratrol, a decrease in BMI was noticed from 29.42 kg/m2 to 29.31 kg/m2 (p > 0.05), resulting in an ES = 0.02.In the next 6 months, in the group receiving 100 mg resveratrol, a decrease in BMI was noticed from 27.97 kg/m2 to 26.75 kg/m2, p < 0.05) in the group receiving 100 mg resveratrol, 0.87  in the group receiving 200 mg resveratrol, and 0.14  in the group without resveratrol supplementation . In the resveratrol groups, the lipid profile improved significantly over the 12 months (p < 0.05), compared with the control group  Basal Glycemia. In diabetic patients, blood glucose levels decreased insignificantly (p > 0.05) after 6 months, in the group receiving 100 mg resveratrol  and in the group receiving 200 mg . The decrease was significant compared to the control group where the blood glucose values had a minimal decrease .In the following 6 months, blood glucose decreased in the group receiving 100 mg resveratrol from 136.29 to 134.24 mg/dL, ES = 0.17, and in the group receiving 200 mg from 136.21 to 133.89 mg/dL, ES = 0.16. In the group without resveratrol, the effect was ES = 0.08 (from 141.81 to 140.58 mg/dL).p > 0.05) in the group receiving 100 mg resveratrol, 0.56 in the group receiving 200 mg resveratrol  and 0.12  in the group without resveratrol supplementation.Comparing the results of the initial evaluation and 12 months of assessment in diabetic patients, ES was 0.52  after 6 months in the group receiving 100 mg of resveratrol  and in the group receiving 200 mg . The decrease was significant from the baseline and from the control group where the blood glucose values had a minimal decrease .In nondiabetic patients, blood glucose levels decreased significantly . In both groups with resveratrol, the decrease was significant compared to the value at six months and compared to the control group .p < 0.05) in the group receiving 100 mg resveratrol, 0.97  in the group receiving 200 mg resveratrol, and 0.20  in the group without resveratrol supplementation.Comparing the results of the initial evaluation and 12 months of assessment in nondiabetic patients, ES was 0.85  Glycosylated Hemoglobin. Glycated hemoglobin values decreased after 6 months as follows: in the group receiving 100 mg resveratrol from 7.13% to 6.72% ; in the group receiving 200 mg resveratrol from 7.15% to 6.66% ; and in the group without resveratrol supplementation, there was a minimal decrease in HbA1c values .p > 0.05, ES = 0.08) and from 6.66% to 6.55%  in the group receiving 200 mg resveratrol. In the group without resveratrol supplementation, ES was 0.03   .p > 0.05) in the group receiving 100 mg resveratrol, 0.40  in the group receiving 200 mg resveratrol, and 0.03  in the group without resveratrol supplementation.Comparing data at the initial evaluation and at 12 months of treatment, ES was 0.34  in poststroke patients.Our study demonstrated that the association of resveratrol to allopathic treatment for 12 months resulted in a significant decrease in BP  found that the administration of 200 mg/kg/day of resveratrol caused an increase in basal metabolism and energy consumption, suggesting a potential mechanism for weight loss [Dyslipidemia and steatohepatitis determined by the atherogenic diet in mice were improved by resveratrol, its beneficial effects being correlated with the altered expression of hepatic genes implicated in lipid metabolism . In obesght loss , 56. In Six months after starting resveratrol administration, a significant decrease in cholesterol and lipid profile change in favor of the patient was observed. Significant values in lipid profile changes were even more noticeable 12 months after resveratrol administration started. In order to define the lipid profile, a great importance had the increase in HDL cholesterol values and decrease in LDL cholesterol, respectively, after the administration of resveratrol. Triglyceride levels decreased significantly in the groups of patients with resveratrol. Between the two resveratrol groups, there were significant differences in triglyceride values, which meaningfully decreased in the group with 200 mg/day/patient.β cells, and impedes complications of the disease [In the literature, it has been highlighted that resveratrol has antidiabetic effects, with a positive effect on both insulin and pancreatic  disease , 58. Res disease .Data existing in the literature show diverse effective doses of resveratrol in insulin-resistant individuals. Thus, it is important to understand that the mechanisms of action of resveratrol can vary according to its dose. Treatment with lower doses of resveratrol activates SIRT1, while higher doses activate AMPK in a way independent of SIRT1 .We noticed a decrease in blood glucose levels, both in the first and last six months of administration. The changes were not significant in diabetic patients for none of the studied groups; however, the effect was more favorable in the resveratrol groups. Oxidative stress plays an important role in the development of insulin resistance and can be neutralized by the antioxidant effect of resveratrol , 61. PreRecommendations: six months after starting resveratrol administration, a significant decrease in cholesterol and LDL cholesterol was observed. Thus, adding resveratrol to standard lipid-lowering medication may help restoring normal lipid profile values in these patients . ObservaThe supplementation of allopathic treatment with resveratrol had a beneficial effect on all monitored parameters that serve as major risk factors for stroke. The response to treatment was better in the 200 mg resveratrol group, but the differences were not significant compared to the 100 mg resveratrol group. Therefore, resveratrol can be used as a complementary, long-term therapy, as well as a beneficial adjuvant in the secondary prevention of stroke.


Recently, some studies have focused on the relationship between dietary protein intake and the risk of type 2 diabetes mellitus (T2DM), but the conclusions have been inconsistent. Therefore, in this paper, a systematic review and meta-analysis of cohort studies regarding protein consumption and T2DM risk are conducted in order to present the association between them. We searched the PubMed and Embase databases for cohort studies on dietary protein, high-protein food consumption and risk of T2DM, up to July 2017. A summary of relative risks was compiled by the fixed-effect model or random-effect model. Eleven cohort studies regarded protein intake and T2DM . The summary RR and 95% CI  of T2DM was 1.12 (1.08–1.17) in all subjects, 1.13 (1.04–1.24) in men, and 1.09 (1.04–1.15) in women for total protein; 1.14 (1.09–1.19) in all subjects, 1.23 (1.09–1.38) in men, and 1.11 (1.03–1.19) in women for animal protein; 0.96 (0.88–1.06) in all subjects, 0.98 (0.72–1.34) in men, and 0.92 (0.86–0.98) in women for plant protein. We also compared the association between different food sources of protein and the risk of T2DM. The summary RR (Relative Risk) and 95% CI of T2DM was 1.22 (1.09–1.36) for red meat, 1.39 (1.29–1.49) for processed meat, 1.03 (0.89–1.17) for fish, 1.03 (0.64–1.67) for egg, 0.89 (0.84–0.94) for total dairy products, 0.87 (0.78–0.96) for whole milk, 0.83 (0.70–0.98) for yogurt, 0.74 (0.59–0.93) in women for soy. This meta-analysis shows that total protein and animal protein could increase the risk of T2DM in both males and females, and plant protein decreases the risk of T2DM in females. The association between high-protein food types and T2DM are also different. Red meat and processed meat are risk factors of T2DM, and soy, dairy and dairy products are the protective factors of T2DM. Egg and fish intake are not associated with a decreased risk of T2DM. This research indicates the type of dietary protein and food sources of protein that should be considered for the prevention of diabetes. Type 2 diabetes is rapidly increasing in the world. Ninety percent of patients with diabetes have type 2 diabetes . Type 2 Evidence suggests that dietary factors may influence the risk of T2DM . Many prIn the present study, we conducted a systematic review and meta-analysis of cohort studies to clarify the association of protein consumption with risk of T2DM. In order to provide a better dietary instruction for the lay public, we also conducted a systematic review and meta-analysis of cohort studies to study the association between different kinds of high-protein food and the risk of T2DM.We searched relevant studies from the Embase and PubMed electronic databases from their starting dates to July 2017. Protein: search items included: ‘dietary protein’ or ‘protein intake’ or ‘plant protein’ or ‘animal protein’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Meat: search items included: ‘red meat’ or ‘processed meat’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Fish: search items included: ‘fish’ or ‘seafood’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Egg: search items included: ‘egg’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Dairy: search items included: ‘dairy’ or ‘milk’ or ‘dairy product’ or ‘yogurt’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Soy: search items included: ‘soy’ or ‘legume’ or ‘soy product’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Eligible studies are selected by further manual scanning of all included studies and relevant reference lists.All included studies should match the following criteria:(1) the study must have a cohort design; (2) the endpoint of the study was the incidence or mortality of the T2DM; (3) the study had to report risk ratios and the results had to be within a 95% CI in the paper ; and (4) the study had to show the participants’ intake of dietary protein or other high-protein foods such as meat, fish, egg, dairy, soy.We extracted the following information from each publication we selected: first the author of the publication, publication year, the country where the study was conducted, the number of the samples and cases, T2DM diagnosis and criteria, the year the study began and finished, the years of follow-up, the methods of diet exposure assessment, the RRs and 95% CI, and the adjustment factors .p < 0.05 was considered statistically significant. The black square in the forest plots represents the weight contribution of every study. In order to evaluate the extent of variability, the I2-test statistic was adapted to estimate the heterogeneity [I2 < 50% and p > 0.05, there was no heterogeneity; we used the fixed-effect model. The random-effect model was selected when I2 > 50% or p < 0.05. We used the Egger linear regression test and Begg rank correlation test to search for publication bias. When p > 0.05, there was no publication bias. Comprehensive Meta-Analysis V2 was employed for data analysis.We used effect models to calculate the summary RRs and 95% confidence to compare the highest dietary protein consumption with the lowest dietary protein consumption . Two-sidogeneity .When I2 We included 11 cohort studies in the analysis . Six of Total Protein Eleven cohort studies [I2 = 18.72, p = 0.25) in all subjects, 1.13 (1.04–1.24)  in men, and 1.09(1.04–1.15)  in women. studies ,16,17,1804–1.24) b (I2 = 104–1.15) c  in all subjects, 1.23 (1.09–1.38)  in men, and 1.11 (1.03–1.19)  in women. studies ,16,17,1809–1.38) b, (I2 = 03–1.19) c,  in all subjects. We used a sensitivity analysis to exclude the most influential studies: the summary RR and 95% CI ranged from 0.92 (0.87–0.98) when the European men’s study [I2 = 14.85, p = 0.31). The summary RR and 95% CI was 0.98 (0.72–1.34)  in men. A sensitivity analysis was used to exclude the most influential studies: the summary RR and 95% CI ranged from 0.88 (0.76–1.02) when the European study [I2 = 45.72, p = 0.12) in women. studies ,16,17,18’s study  was excl’s study  was excl’s study  and when72–1.34) b . We used a sensitivity analysis to exclude the most influential studies: the summary RR and 95% CI ranged from1.20 (1.07–1.35) when the U.S. Study [I2 = 27.68, p = 0.17). studies ,29,30,31S. Study  was exclS. Study  was exclS. Study , and wheProcessed Meat Eleven cohort studies [I2 = 49.32, p = 0.03). studies ,29,30,31Fish Nine cohort studies [I2 = 79.71, p < 0.001). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from0.99 (0.89–1.10) when the American study [I2 = 79.41, p< 0.001). studies ,36,37,38an study  was exclan study  was exclan study , and wheEgg Five cohort studies [I2 = 91.12, p< 0.001). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from 0.97 (0.49–1.88) when the Lithuanian study [I2 = 69.43, p = 0.06). studies ,40,41,42an study  was exclTotalDairyProduct Consumption Eleven cohort studies [I2 = 48.81, p = 0.03).  studies ,50,51,52Whole Milk Seven cohort studies [I2 = 52.20, p = 0.01). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from 0.85 (0.76–0.94) when the American study [I2 = 46.87, p = 0.04). studies ,46,47,48an study  was exclYogurt Seven cohort studies [I2 = 62.06, p = 0.01). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from 0.81 (0.67–0.97) when the Japanese study [I2 = 40.61, p = 0.11). studies ,47,48,49Soy Eight cohort studies [I2 = 86.57, p < 0.01). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from0.82 (0.68–0.98) when the American study [I2 = 69.34, p < 0.01). However, the summary RR for high vs. low soy was 0.74 (0.59–0.93)  in women. We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from0.66 (0.49–0.90) when the American study [I2 = 0.00, p = 0.67). studies ,55,56,57an study  was excl59–0.93) b, (I2 = an study  was exclNo significant publication bias was detected in the Begg-Mazumdar’s test and Egger’s test of our meta-analyses, provided in According to the results of this meta-analysis, the intake of total protein and animal protein was associated with a high risk of T2DM both in males and females. The intake of plant protein was associated with low risk of T2DM in females, but not in males. In high animal protein food, red meat, and processed meat were associated with a high risk of T2DM in all subjects, while total dairy products, low-fat dairy, and yogurt were associated with a low risk of T2DM in all subjects, and egg and fish were not associated with a decreased risk of T2DM. In high plant protein food, soy was associated with a low risk of T2DM in females.Higher intake of dietary protein is often associated with lifestyles, including physical activity, body weight, smoking, drinking. For example, we already know that overweight and obesity are risk factors for T2DM, and a meta-analysis showed that each unit increase of BMI would increase the risk for T2DM by approximately 20% . In our The statistical power of the results could be significantly increased as the number of studies and the sample size increase, but it could also lead to heterogeneity. Some heterogeneity was due to different participants’ characteristics and regions, and different dietary assessment methods. Thus, heterogeneity is usually used to explain the study characteristics and is difficult to interpret. In our study, the heterogeneities of total protein and animal protein were in the acceptable range, but the heterogeneity of plant protein was outside of the range. We found that the European study  contribuAs meta-analysis is based on published studies, the publication bias affect is inevitable. It is particularly important to evaluate publication bias. In our meta-analysis, we used the Egger linear regression test and Begg rank correlation test to determine publication bias, and we found that there was no publication bias in our study.Dietary protein and amino acids are involved in the modulations of insulin sensitivity and glucose metabolism. However, the results from human studies were still inconsistent. Some studies showed that high intake of dietary protein had negative effects on glucose homeostasis by facilitating insulin resistance and increasing gluconeogenesis ,59,60. AFrom the current literature available, the inconsistency in association between plant protein and T2DM was probably due to gender difference. The negative association between plant protein or soy and T2DM was observed mainly in women, while most of the studies in men found null results. Therefore, results from all subjects without considering gender difference were different and the proportion of women in the study may influence the results. However, the exact mechanism is unclear.In order to further refine and compare the association between different food sources of protein and T2DM, and facilitate dietary guidance, we have analyzed the relationship between different high-protein food and the risk of type 2 diabetes. We found that different high-protein foods play different roles in T2DM, even if they are all animal-based foods. Our results indicated that the intake of red meat and processed meat are risk factors for T2DM. They are also positively associated with weight gain , stroke Our meta-analysis also had limitations. First, publications into our research were adjusted for BMI, but some studies had measurement errors because of self-reporting of height and weight, resulting in the BMI relying on self-reporting, which could lead to confounding results. Second, some important factors that influence T2DM such as fiber, lipids, and carbohydrates, were only adjusted in some of these studies, which may also lead to confounding results. Additionally, limitations might be due to temporal bias. Studies with longer follow-up might beless influenced by temporal bias. In our studies, the follow-up period of each research study was different, so the temporal bias might impact the association between dietary protein intake and the risk of T2DM.In summary, we found that total protein and animal protein consumption were the risk factors for T2DM, and plant protein was the protective factor for T2DM in women, but not in men. We also found that different high-protein foods have a different effect on T2DM risk, even if they all belong to animal proteins. These results underline the significance of taking into account what kind of dietary protein and food sources of protein are recommended for the prevention of diabetes.