There is growing interest in how social processes and behaviour might be affected in Parkinson’s disease. A task which has been widely used to assess how people orient attention in response to social cues is the spatial cueing task. Socially relevant directional cues, such as a picture of someone gazing or pointing to the left or the right have been shown to cause orienting of visual attention in the cued direction. The basal ganglia may play a role in responding to such directional cues, but no studies to date have examined whether similar social cueing effects are seen in people with Parkinson’s disease. In this study, patients and healthy controls completed a prosaccade (Experiment 1) and an antisaccade task (Experiment 2) in which the target was preceded by arrow, eye gaze or pointing finger cues. Patients showed increased errors and response times for antisaccades but not prosaccades. Healthy participants made most anticipatory errors on pointing finger cue trials, but Parkinson's patients were equally affected by arrow, eye gaze and pointing cues. It is concluded that Parkinson's patients have a reduced ability to suppress responding to directional cues, but this effect is not specific to social cues. Parkinson’s disease (PD) is a progressive neurodegenerative disorder with insidious onset  or incongruent (opposite) direction to the saccade target. For prosaccades, normal amplitude and response latency are typically reported in PD  and 13 healthy controls  took part in the study. Fifteen patients and 10 controls were recruited from Izmir with 8 patients and 3 older controls coming from the UK. Symptom severity was assessed using the United Parkinson's Disease Rating Scale III (UPDRS III motor score)  and the ethics committee of the Dokuz Eylül University Hospital (Turkey), in accordance to the principles of the Declaration of Helsinki.Subjects were tested using an Eyelink 1000 (Lincoln) or EyeLink 1000 Plus (Izmir) eye-tracker . The viewing distance to the monitor was approximately 60 cm. Stimuli were presented on a 19 inch LCD monitor  attached to a Dell PC. The pupil movement was tracked using infrared camera with a frequency of 1000 Hz. A chin rest was used to keep the head in a stable position. Corel Paint Shop Pro X image editing software was used to create the cue stimuli used. The eye gaze images were colour photographs of a Caucasian male face cropped to show only the eyes, subtending 5.52° of visual angle. The arrow cues were based on the left and right road directional signage used in the UK, Europe and Turkey, comprising a blue circle with a white arrow. The arrow cues items subtended a visual angle of 4.45°. The finger pointing stimuli consisted of colour image of a whole male hand, with a pointing indexing finger subtending 5.52° visual angle  of either 100 or 500 ms, the central fixation cross was extinguished and a black dot with a diameter of 1.26° of visual angle. The dot could appear vertically centred at either the left or right of the screen at an eccentricity of 14.75° of visual angle, where it remained for 2000 ms after which both the target and cue were extinguished. The target dot appeared on the left and right sides of the screen with equal probability within a block. Participants were instructed to “maintain fixation on the central cross and ignore the road signs, fingers and eyes until the dot appeared, when they should look at the dot with their eyes". Participants were also informed that the direction to which the arrows or eyes pointed did not predict the likely position of the target dot. An interval of 1000 ms separated the beginning of one trial and the start of the next. A schematic of the events on a prosaccade task trial is shown in Fig. Saccade parameters were extracted off-line with Dataviewer software . Saccades were detected as those eye movements which exceeded a velocity of 30°/s and an acceleration of 8000°/s/s. The first saccade detected by Dataviewer following cue onset was taken as the response for each trial for each participant and only those which occurred between 80 and 1500 ms . Equivalent Bayesian analyses were completed using JASP (Version 0.13.1) software to calculate inclusion Bayes Factors (BFincl). For each main effect and interaction effect the BFincl provides an indication of how much more likely models which include the effect are, relative to the average likelihood of all models that do not include the effect (relative to the null hypothesis model). In the case of interaction effects, the likelihood comparison is made between models that include the interaction and those which include at least one of the component effects of the interaction as main effects or in lower order interactions (the “across matched models” option in JASP). Although there are no hard boundaries for considering the statistical significance of Bayes Factors, values between 0.33 and 3 are generally considered to offer weak evidence for distinguishing between the modelled and null hypotheses, whereas values greater than 3 or less than 0.33 are considered strong evidence for or against the tested model.Analysis of Variance (ANOVA), F = 0.483, p = 0.483, incl = 0.07), congruency  = 1.320, p = 0.259, incl = 0.27) or Group  = 0.035, p = 0.855, incl = 0.26). The interaction between cue type and congruency was found to be significant for SRTs  = 3.21, p = 0.047, incl = 0.74). Means comparisons showed that Finger cues were the only condition to show a trend towards a significant congruency effect overall (Eyes: t(33) = − 0.268, p = 0.884; Arrows t(33) = − 0.105, p = 0.917; Fingers t(33) = 1.80, p = 0.081) , saccadic reaction times (SRTs) were pooled across trials with different SOAs (100/500 ms) to obtain a mean SRT value for each participant in each of the cue type and congruency conditions. A three-way mixed Analysis of Variance (ANOVA) with 2 repeated measures factors: cue type , cue-target direction congruency  and one between participants factor: Group  showed no main effect of cue type (away from the target) was analysed using a 4-way Analysis of Variance (ANOVA). This showed a main effect of congruency  = 4.69, p = 0.037, incl = 2.33) and a significant interaction effect between SOA, congruency and Group  = 4.867, p = 0.034, incl = 1.73). Whereas healthy older participants showed a trend towards a congruency effect on directional errors at the shortest SOA (100 ms) (t(12) = 1.74, p = 0.108) and no difference in errors at the longest (500 ms) SOA, Parkinson's patients showed a significant congruency effect at the longest SOA only (t(22) = 2.86, p = 0.009)  = 2.97, p = 0.06, incl = 0.29). Means comparisons revealed a significant cue congruency effect on directional errors for Fingers but not the other cue types (Eyes: t(34) = − 0.442, p = 0.661; Arrows t(35) = 0.442, p = 0.661; Fingers t(34) = 3.43, p = 0.002).The ANOVA also revealed a trend towards an interaction between cue type and congruency which was similar to the interaction for SRTs reported above  = 70.20, p < 0.001, incl = 1.24exp27) with anticipatory errors being increased for trials with a 500 ms versus a 100 ms SOA interval. There was also a strong effect of cue type  = 8.644, p < 0.0001, 0.20; BFincl=12.55), as well as an interaction between cue type and SOA  = 12.92, p < 0.0001; ηp = 0.03, BFincl = 2739.31). Means comparisons for the size of the effect of SOA on errors showed that this was significantly greater for Finger cues compared to the two other cue types (Fingers versus Eyes: t(35) = 5.20, p < 0.001; Fingers versus Arrows: t(35) = 2.96, p = 0.005) , p = 0.069, ηp = 0.0070, BFincl = 0.62). For Control participants anticipatory errors were increased for Pointing cues relative to both Eye (t(12) = 3.36, p = 0.006) and Arrow cues (t(12) = 2.93, p = 0.010), whereas for PDs there were no significant differences between Anticipatory errors for any of the cue types . This showed a significant main effect of SOA .Pearson correlations were carried out to investigate associations between Parkinson’s disease severity (UPDRS III scores) and cue congruency for each of the three cue types  for the 3 dependent measures of SRTs, anticipatory errors and directional errors. None of these correlations showed a significant association between UPDRS and cue congruency effects at standard significance level ; The size of cueing effects on directional errors for Arrow cues and MMSE scores ; The size of cueing effects on directional errors for Eye Gaze cues with backward digit span and semantic verbal fluency scores ; and between Stroop task interference and anticipatory errors for Arrow cues .Similarly, correlations were carried out between the size of the eye movement cueing effects and scores on cognitive tests for the sub-set of patients completing the neuropsychological tests see “” along waway from the target) and these errors were found to be more common on cue-target incongruent trials, particularly for pointing finger cues. This suggests that saccade responses were being initiated in response to the cue rather than the target. Consistent with this, rates of anticipatory errors (defined as SRTs < 80 ms) were high overall. Anticipatory errors were particularly common for Pointing cues and and for trials with the longest SOA interval  affected saccades. Finger pointing cues showed the greatest cue-congruency effect on SRTs for both PDs and healthy controls, with a trend towards longer SRTs on incongruently cued trials for pointing fingers but not the other cue types Fig. a. Surprial Table . AnotherPrevious work has shown cue congruency effects on SRTs for eye gaze cues , but this influence then decays (or is actively suppressed) within 500 ms. For Parkinson's patients though, the influence of the central cue seems to persist longer and was greatest for 500 ms SOA trials. This could be due to either a slower decay or a reduced ability to actively inhibit the programming of a saccade in the cue direction in patients. Another way of viewing this is that some of the directional errors at the longest SOA period in patients might be considered “anticipatory” in that they reflect generation of a saccade in the direction of the pre-cue, even though their SRTs were longer than the generally assumed cut-off criteria for deeming a response to be anticipatory.Apart from this interaction with SOA, the effects of arrows, eye gaze and pointing cues did not differ greatly between patients and controls. From this is might be concluded that the processing of social cues is not differentially affected relative to non-social cues in PD, but this interpretation may be too straightforward. Other work has also found that social processing deficits are only apparent in PDs under conditions where a strongly cued response must be inhibited. For example, Foley et al.  report tt = 4.11, df = 80, p < 0.001) although all of the patients scored greater than 23 on the MMSE. Comparisons of digit span, stroop interference and verbal fluency test scores showed no significant differences between groups (digit forward t(42) = 0.81, p = 0.425; digit backward t(42) = 1.13, p = 0.267; Stroop t(33) = 0.732, p = 0.470; fluency t(33) = 1.36, p = 0.183). All other aspects of the task and procedure were identical to Experiment 1 except for the instruction given to the participant. When the target stimulus appeared following presentation of the central pre-cue , participants were instructed to make a saccade directly away from the target and to fixate the location opposite to that at which the target appeared. A schematic of the events on an antisaccade task trial is shown in Fig. Twenty-one patients with mild to moderately severe Parkinson’s disease  and 31 healthy controls  participated in Experiment 2. Fifteen patients and 28 controls were recruited from Izmir with 6 patients and 3 older controls coming from Lincoln. Four of the patients and one of the controls had also previously completed the prosaccade task (Experiment 1) either in the same testing session or at a previous testing session. MMSE scores were obtained for all patients and controls and additional neuropsychological testing was carried out in 30 of the control participants and 14 of the patients. MMSE test scores were found to be significantly lower in the patient relative to the control group  = 5.29, p = 0.028, 0.14; BFincl = 0.57) with longer SRTs on incongruent versus congruent trials . A significant main effect of Group was also found with PDs showing significantly slower SRTs in the antisaccade task compared to healthy controls  = 7.435, p = 0.010, incl = 4.55) Parkinson's: M: 416 ms ± 19 Controls: M: 357 ms ± 10.2). There was also a marginally significant interaction between cue type and congruency  = 2.57, p = 0.084, incl = 2.59). Means comparisons showed a significant congruency effect for finger cues (t(38) = 2.72, p = 0.01) but no significant effects for eyes (t(38) = 0.235, p = 0.815) or arrows (t(38) = − 0.121, p = 0.904)  = 84.80, p < 0.001, incl = 3.53exp26) along with a significant main effect of Group  = 10.746, p = 0.002, incl = 14.93), with PDs making more errors relative to controls (Parkinson's M: 25.24% ± 2.56 M: Controls 14.36% ± 2.10). Group did not interact with any other repeated measures factor. However, there was found to be a significant interaction between SOA and congruency  = 6.39, p = 0.015, incl = 1.96) and a 3-way interaction between cue type, SOA and congruency  = 4.08, p = 0.020, incl = 2.08). These interactions reflected a trend towards increased errors on congruent compared to incongruent trials at the short SOA, but significantly increased errors on incongruent compared to congruent trials at the longer 500 ms SOA (t(87) = 2.64, p = 0.010). This effect of SOA on congruency was strongest for the Arrow and Pointing finger cues but was not apparent for Eye gaze cues  = 8.783, p < 0.001, incl = 27.03) and a main effect of SOA  = 98.72, p < 0.001, incl = 1.26exp46) on anticipatory errors. There was also a main effect of Group  = 6.543, p = 0.014, incl = 3.81), a significant 2-way interaction between cue type and SOA  = 14.60, p < 0.001, incl = 126,608.33 and a significant interaction between cue type and Group  = 4.75, p = 0.011, incl = 1.25). The later 2-way interaction reflected the fact that anticipatory responding was similarly high for all cue types in PDs (Fingers versus Eyes: t(20) = 0.71, p = 0.487; Fingers versus Arrow: t(20) = 0.76, p = 0.46; Arrow versus Eyes: t(20) = 0.11, p = 0.91), whereas errors were greatest for the Finger and Arrow cues relative to Eye gaze cues in older controls (Fingers versus Eyes: t(30) = 5.04, p < 0.001; Fingers versus Arrow: t(30) = 3.71, p = 0.046; Arrow versus Eyes: t(20) = 2.07, p = 0.048) .Pearson correlations were also carried out to investigate associations between Parkinson’s disease severity (UPDRS III scores) and cue congruency effects for each of the three cue types  for the 3 dependent measures: SRTs, anticipatory errors and directional errors. None of the correlations showed a significant association between UPDRS and cue congruency effects at standard significance level  and the congruency effect on directional errors for Finger cues , with all other correlations tested being statistically insignificant at standard significance level (α = 0.05).Correlations were carried out between the size of the eye movement cueing effects and scores on cognitive tests for the sub-set of patients completing the neuropsychological tests and for the MMSE for all patients. These showed correlations between MMSE scores and the congruency effect on SRTs for Arrow Cues , Group , cue type  and cue congruency (congruent/incongruent) as factors, were used to compare the findings of Experiment 1 and 2 for each of the dependent measures. As only four of the patients and one of the controls took part in both Experiments, Task was entered as between subject factors alongside Group, with cue type and congruency treated as repeated measures factors.F = 4.08, p = 0.048, incl = 1.23), directional errors  = 6.42, p = 0.013, incl = 3.62) and anticipatory errors  = 4.701, p = 0.033, incl = 2.27), reflecting increased errors and SRTs for patients in the antisaccade task only. SRTs also showed a significant cue type by congruency interaction effect  = 5.68, p = 0.004, incl = 10.33), due to the larger cueing effects for the Pointing finger cues for both participant groups across both tasks. The analysis of anticipatory errors further showed a 4 way interaction between Task, Group, cue type and congruency  = 3.046, p = 0.05, incl = 1.46).This analysis confirmed the existence of Task by Group interactions for SRTs , in Experiment 2 antisaccade directional errors and SRTs were significantly increased in PDs relative to controls. Some differences in the effect of the different types of cue images presented on each trial were also observed, with arrows and fingers generally showing stronger effects than eye gaze cues. Patients also differed significantly from controls in terms of the influence of the different cue types on the occurrence of anticipatory saccades, whereby controls made more anticipatory responses to finger cues, where anticipatory responding was equally high for all cue types in PDs Fig. .Although there is some inconsistency in the literature concerning whether antisaccade errors are increased in PD, the majority of studies report significantly increased directional errors in the task  were significantly increased in patients. There was also an interesting interaction effect for these errors between Group and cue type. Superficially the presence of this interaction might suggest that patients have a selective impairment in the processing of one or other of the different cue types. However, closer inspection of the interaction suggests a different explanation. Whereas control participants were particularly prone to making anticipatory saccades when a pointing finger image was presented compared to eye gaze or arrow cues, Parkinson’s patients appeared equally distractible under all three cue conditions Fig. . This isRather than any selective impairment in processing of socially relevant cues or stimuli with an action affordance association  were observed for main effects of cue type, SOA and Group and interactions between SOA and cue type and congruency, suggesting that the data offer strong evidence in favour of models which include these effects, although higher order interactions  were inconclusive relative to alternative models (BFincl between 0.33 and 3). The addition of “catch trials”, where a cue is presented but no target appears on a minority of trials would also have been useful to determine whether saccade responses were anticipatory or were driven by the onset of the target stimulus. It is likely that many directional errors are likely to have been responses executed in response to the cue even though they had longer response times than the most commonly used cut-off for deeming a saccade to be anticipatory (< 80 ms). Finally, the limited additional neuropsychological testing carried out in a sub-set of patients revealed a number of significant correlations with eye movement task measures . This suggests that relatively simple tests of eye movements and spatial attention might be sensitive to onset of cognitive decline in patients. Application of comprehensive standardised cognitive tests across all participants would, therefore, have been informative for establishing whether attentional/oculomotor deficits in patients relate to the presence of cognitive impairments.A number of limitations of the present work can be identified. We acknowledge that participant and trial numbers used in the current study were low and as such the study may suffer from low statistical power. However, high inclusive Bayes factors values (BFIn summary, the results overall suggest that Parkinsons patients may be less able to suppress the influence of directional attentional cues on eye movement generation, but that this effect is not specific to cues of a sociobiological nature. Although the findings do not indicate any specific deficit in social attention in patients, general visuospatial attentional difficulties might translate into real world problems for patients when faced with situations in which eye movements and visual attention need to be directed within a busy environment, including commonly encountered social situations. This may be particularly challenging in situations where there are demands on executive and inhibitory control mechanisms, for example in a cluttered visual environment or when trying to pay attention to a task whilst having a conversation. Research using real world eye tracking in social situations may prove particularly informative in understanding how such deficits translate into real world problems for Parkinsons patients.


To evaluate the relationship between gingival phenotype and tooth location based on selected index teeth (“Ramfjord”) and assess possible differences between women and men.Thirty‐six women and 20 men voluntarily participated in this investigation with an average age of 23 years . Gingival phenotypes (GP) were assessed by transparency of a periodontal probe through the buccal gingival margin.p = 0.006) showed a thick GP, premolars  as well as incisors  were predominantly categorized as thin. In addition, significantly thicker GP was in general observed for maxillary teeth (p = 0.001) but without differences between genders (p = 0.722).A comparable and similar GP on all index teeth was only found in seven out of the 56 subjects, that is, thin or thick only: Five participants  showed a uniform and constantly thick and two females a constantly thin GP. While the majority of molars (94.6%; No constant GP can be expected within one dentition. The use of the “Ramfjord teeth” may serve as a quick overview and reliable method to screen GP distribution. We hypothesized that there is a general GP within one dentition and that index teeth may serve as a full‐mouth screening option.22.1In this cross‐sectional observational study, dental students were randomly screened on a voluntary basis and the distribution of thin and thick gingival phenotype appearance within one dentition in selected teeth (“Ramfjord teeth”) was assessed.2.2Pregnant female volunteers,Subjects with cervical fillings or crownsTooth crowding and misalignment,Intake of any medication affecting the soft tissue health ,Volunteers with either signs of periodontal disease defined as periodontal probing depths >3 mm or gingival recessions,Heavy smokers (>10 cigarettes/day).Each participant signed informed consent form after thorough explanation of the nature, risks, and benefit of this clinical investigation.The Witten/Herdecke University's Ethical Committee of the Medical Faculty approved the consent form and study protocol (34/2015). Sixty‐four dental students out of the clinical years (3rd to 5th year) of the Witten/Herdecke University were screened for eligibility. The following exclusion criteria were applied:2.3All subjects received oral hygiene instructions and, if needed, and cleaning and polishing of all teeth.After calibration using digital photographs, one dentist (KF) obtained all clinical parameters. Five test‐subjects were examined prior starting with enrolment and data collection.2.4Primary outcome measure: To assess the gingival phenotype based in probe transparency through the gingival margin within on dentition measured on index teeth .Secondary outcome measures: connection between the gingival phenotype, tooth position in view of the individual gender.Gingival phenotypes (GP) were dichotomously categorized as thin or thick based on the visible transparency of a periodontal probe at teeth 16, 21, 24, 36, 41, and 44   was measured to the nearest 0.5 mm with a millimeter‐scaled pressure controlled periodontal probe ,2. GT was determined before phenotype assessment to avoid any bias.Gingival thickness (GT) was measured at tooth 21 using a customized digital caliper with a minimal spring force of 4 g/mmGingival width (GW) was evaluated mid‐buccally to the nearest of 0.5 mm with a periodontal probe at tooth 21. GW was defined as the distance between the free gingival margin and the mucogingival junction . Mean GT21 was 0.6 mm (SD: 0.17 mm) and mean GW21 was 4.73 mm (SD: 1.25). Furthermore, GT21 and GW21 appeared to be significantly directly correlated .After screening, four volunteers dropped out because of restorations at tooth 21  displayed a uniform and constantly thick GP, whereas two female participants had a constantly thin GP. No male participant showed a uniformly thin phenotype.p = 0.001).Regarding phenotype distribution in the upper and lower jaw, more maxillary teeth showed a thick phenotype (59%), whereas in the lower jaw, the distribution was almost equal with 49.4% thick and 50.6% thin phenotypes, respectively (p = 0.006). In contrast, 61.6% of premolars (24: 51.7%/44: 67.5%) and 70.5% of incisors (21:61.1%/41:76.8%) were categorized as thin. While no statically significant difference was found for the premolar area (p = 0.09), slightly more thin GP were found for the incisor area (p = 0.046)  were categorized as thick with a highly significant difference (p = 0.722) nor with respect to tooth side (16: p = 0.55/21: p = 0.15/24: p = 0.41/36: p = 1.00/41: p = 0.53/44: p = 0.36).Regarding the differences between female versus male participants, no statistical significant difference was found, neither in general (4In this investigation, we sought to assess the relation between clinically estimated gingival phenotype and its distribution within one dentition using the “Ramfjord” index teeth and compared the outcomes of women and men. To the authors best knowledge, this is the first clinical investigation reporting on phenotype distribution in the upper and lower jaw as well as in molars, premolars and incisors, respectively – also with regard to gender differences.Based on our findings, the hypotheses of a uniform GP within one appears very questionable. Constant GP was found in only five out of 56 participants. While the clear majority of molars were categorized as having a thick GP, most of incisors and premolars were found to have a thin GP, respectively. More maxillary teeth seem to have a thick GP and, in contrast, a nearly equal distribution was reported for the mandible. Regarding potential differences between female and male participants, no statistical significant difference was found. Nevertheless, comparing the percentages of thick versus thin GP in relation to gender, a tendency for more widespread thick GP can be observed for male participants Figure .The clinical assessment of gingival biotypes using a periodontal probe was introduced as an easy and low‐cost technique by Kan et al.  in all dLimitations of this report might be the relative small sample size and the sole application of a single assessment method based on probe transparency by only one clinician without testing for intra‐rater repeatability. Inter‐rater reliability could not be tested because of the study design. Past orthodontic treatment, furthermore, was not considered as potentially soft tissue thickness influencing factors. All participants were asked about orthodontic therapies and nearly 90% remembered such a treatment. Hardly any participant, however, could report on applied techniques or movements and, hence, further evaluation was discarded. In addition, crowding and tooth angulation was appraised only clinically, hence might be seen as not objective. Furthermore, we did not evaluate our study population in a case–control manner, and, therefore, this also might be a limitation. Future research might include comparison of gingival configuration around dental implants and natural teeth or around natural teeth before and after orthodontic treatment. To establish a screening protocol based on index teeth, screening of the whole dentition or at least contralateral teeth with regard to GP, GT, and GW might be advisable.In conclusion, diverse gingival thickness must be expected within one dentition with a clear differentiation based on tooth location. An actual screening based on index teeth seems therefore feasible. Molars, in general, have thicker soft tissues than premolars and incisors, respectively, and there seems to be a clear tendency for a thicker phenotype in the maxilla compared to the mandible as well as slight tendency in young men compared to women for maxillary teeth again. Interventional studies need to evaluate the influence of these findings for different dental treatments with larger subject numbers.The authors declare that they have no conflict of interest.Kai Fischer, Anton Friedmann, and Patrick Schmidlin contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Jasmin Büchel, Kai Fischer, Frederic Kauffmann, and Christian Heumann. The first draft of the manuscript was written by Kai Fischer and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.


Nivolumab has shown promising results against non‐small‐cell lung cancer (NSCLC). However, its efficacy to treat central nervous system (CNS) metastases, specifically among programmed cell death 1 ligand 1 (PD‐L1)‐negative patients, remains unclear.A 66‐year‐old woman was diagnosed with adenocarcinoma stage II and underwent a left lower lobectomy. The histopathological evaluation revealed stage IVA with pleural dissemination. The patient did not harbor an epidermal growth factor receptor (EGFR) mutation or anaplastic lymphoma kinase (ALK) rearrangement, and PD‐L1 expression of the surgical specimen using 22C3 assay was 0%. Single brain metastasis was detected, and carboplatin and nab‐paclitaxel were administered. After three cycles, asymptomatic multiple brain metastases were identified, and the patient was treated with nivolumab as second‐line chemotherapy. Six months later, MRI revealed an intracranial complete response (CR). Nivolumab was discontinued after 23 cycles due to immune‐related adverse events (irAEs) of grade 2 rash. However, its effects were sustained for 13 months after discontinuation. We were unable to evaluate the PD‐L1 expression of brain metastases, which may show heterogeneity.This case demonstrates that nivolumab effectively treated a patient with negative PD‐L1 expression of the lung harboring CNS metastases. Based on clinical trials, the effects of nivolumab last longer than that of docetaxel.22 weekly) were administered. However, she developed grade 2 chemotherapy‐induced peripheral neuropathy, and the treatment was discontinued after three cycles. Asymptomatic multiple brain metastases were identified, and the patient was treated with nivolumab  as second‐line therapy. One month later, the MRI scan showed tumor shrinkage of the brain metastases. The tumor PD‐L1 expression of the primary lung tumor was 0% based on the 22C3 assay , without EGFR mutation and ALK translocation  against brain metastases. A summary of studies reporting the efficacy of ICI monotherapy for brain metastases in patients with NSCLC is shown in Table The PD‐L1 status is a predictor of nivolumab efficacy in nonsquamous NSCLC. Some studies have reported discordance in the expression of PD‐L1 between primary lesions and metastatic sites in lung cancer. A study of 73 cases showed that the discordance in the expression of PD‐L1 between primary lung tumors and their corresponding metastatic brain lesions was 14%.NGS has been used to detect driver mutations and TMB that can be used as biomarkers of treatment response. Molecular‐targeted drugs are approved for NSCLC with driver mutations such as EGFR, ALK, ROS1, BRAF, MET, and NTRK, showing remarkable efficacy. Some drugs show intracranial efficacy, which could be an option for patients with CNS metastases. Patients with TMB‐high NSCLC who received ICI therapy showed significantly longer PFS and OS than those with TMB‐low NSCLC,The mechanism behind the effects of PD‐1 inhibitors on brain metastases remains unclear. Preclinical models have shown that ICI activates T‐cell trafficking across the blood–brain barrier without directly acting on the tumor.Unlike cytotoxic chemotherapy, PD‐1 inhibitors elicit a long‐term response, which persists even after treatment discontinuation. The 5‐year survival was 13.4% according to pooled efficacy and safety data from the phase III CheckMate‐017 and CheckMate‐057 trials.To our knowledge, this was the first case report to show complete response to nivolumab against brain metastases in patients with PD‐L1‐negative NSCLC. Further research is needed to better understand the mechanism and identify biomarkers that can better predict the efficacy of nivolumab.Yoshitaka Zenke reports grants from AstraZeneca, MSD K. K., and Merck; personal fees from AstraZeneca, Boehringer‐Ingelheim, Chugai Pharmaceutical Co. Ltd., Eli Lily Co. Ltd., MSD K. K., Ono Pharmaceutical Co., and Bristol‐Meyers Squibb. The other authors have no conflicts of interest to disclose. All authors were involved in manuscript drafting/revising and approved the final manuscript.Conceptualization, R.K., Y.Z., Y.H.; Formal Analysis, R.K.; Resources, T.H., Y.Z.; Writing ‐ Original Draft, R.K.; Writing ‐ Review & Editing, Y.Z., H.Y.; Supervision, Y.Z.All authors had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Informed consent was obtained to publish this report.


Gadus morhua). Cod embryos and larvae were exposed to 0.5 μg/L (low), 2 μg/L (medium), and 6 μg/L (high) Cu from 4 to 17 days post fertilisation (dpf). Hatching success, mortality, oxygen consumption, biometric traits, and malformations were determined. A dynamic energy budget (DEB) model was applied to identify potential impacts on bioenergetics. A positive correlation was found between Cu exposure concentrations and Cu body burden in eggs, but not in larvae. The tested concentrations did not increase mortality in neither embryos nor larvae, or larvae deformations. Further, the DEB model did not indicate effects of the tested Cu concentrations.In this study we investigated potential impacts of Cu exposure at low, environmentally relevant, concentrations on early live stages of Atlantic cod ( HoweverGadus morhua), haddock (Melanogrammus aeglefinus), herring (Clupea harengus) and various species of demersal flatfish, Norwegian fjords are important spawning and nursing grounds. In fish, heavy metals such as Cd and Cu are of concern because it is easily accumulated and may disturb biological mechanisms even at relatively low, environmentally relevant exposure concentrations [For several fish species, including commercially important fish such as Atlantic cod , immunological function, metabolism and ultimately behaviour [Clupea harengus), plaice (Pleuronectes platessa) and gilthead seabream (Sparus aurata) [Oryzias melastigma) [Intracellular Cu concentrations are tightly regulated in animals; however, excessive amounts may disturb protein function and induce oxidative stress . Cu toxiehaviour . Cu is cehaviour . However aurata) ,15. A reastigma) . HoweverThe aim of this study was to investigate whether Cu exposure at low, and thus environmentally relevant concentrations, affect the survival and development of Atlantic cod embryos and larvae. A dynamic energy budget (DEB) model, integrating developmental traits such as body size, yolk sac size, body weight and oxygen consumption, was applied to reveal potential impacts on energy allocation/availability under exposure and potential impacts on growth.22.1Gadus morhua) were obtained from a spawning brood stock of Atlantic cod at Austevoll Research Station (Institute of Marine Research). After fertilization, eggs were incubated for 24 h  before being sent to SINTEF Sealab in cooled containers by air freight. Until the start of the exposure, eggs were kept in 50 L flow-through tanks supplied with filtered (1 μm) natural sea water from Trondheimsfjord . Dead and unfertilized eggs were removed from the tank.Eggs of Atlantic cod (Exposure started at 4 days post fertilisation (3 dpf) and continued throughout the embryonic stage until 17 dpf, corresponding to 5 days post hatch (5 dph). Nominal Cu exposure concentrations were 0.5 μg L/L (low), 2 μg/L (medium), 6 μg/L (high). Natural seawater was used as negative controls (ctrl). The exposure concentrations were based on the guide for classification of environmental conditions in water from the Norwegian Environmental Agency [4×5 H2O  in ultrapure water , which was subsequently diluted with filtered natural seawater to prepare seawater stock solutions. Exposure solutions were prepared by further diluting the seawater stocks. Exposures were performed in 1 L food grade polyethylene terephthalate (PETE) containers partly submerged in a flow through water from a temperature-controlled source (10 °C) water. The containers were thoroughly cleaned before use, rinsed with 1 M HNO3 and soaked with filtered seawater for 24 h. Before exposure onset, the containers were equilibrated for 24 h with respective Cu exposures concentrations to saturate container walls to reduce loss of Cu.Exposure solutions were prepared by dissolving CuSOAt 4 dpf approximately 500 eggs were added to each container and exposed until 5 dph. Each exposure was replicated (n = 7), whereof all replicates were used to determine hatching and mortality. Individuals from one replicate were used for daily determination of biometry and larval deformations as described below. To ensure sufficient oxygen saturation and slight mixing of the water, the exposure containers were supplied with air provided at low pumping rate with a tubing pump releasing single bubbles close to the bottom of the container. Oxygen saturation and temperature were measured daily using a phase fluorometer  equipped with a R-series FOSPOR electrode  and temperature compensated by a thermistor probe . The ambient and water bath temperature was set to 10 °C with dimmed light conditions with a 12 h light/dark cycle. Larvae hatched before the main hatching event were removed from the exposure containers and kept under comparable exposure conditions in separate beakers. Similarly, unhatched eggs after the main hatching event were removed from the main exposure containers and kept separately. The exposure solutions were exchanged at 72 h of exposure, 144 h and at 2 dph, and at each time point approximately 2/3 of the water was exchanged.2.23, ultrapure grade, purified from HNO3  in sub-boiling distillation system, Milestone, SubPur, Sorisole, BG, Italy), to achieve a final concentration of 0.1 M HNO3 and stored in dark at 4 °C prior to analysis. Copper concentrations were analyzed with ICP-HR-MS using a Thermo Finnigan model Element 2 instrument  with a SC2 DX autosampler (with ULPA filter dust cover) and a PrepFAST flow injection analysis system . The instrument was calibrated with multielement standards. As blank samples, ultrapure water  and sea water samples were analyzed together with the samples. The determined LOD for Cu in seawater was 0.6 μg/L.Water samples were taken 4 times during the experiment, namely at the start (0 h), at 72 h, 144 h and at the end of the exposure, each before and after changing of the exposure solution, to determine dissolved Cu concentrations and eventual changes caused by exposure solution exchange. At each timepoint, 10 mL were taken from three exposure containers (n = 3) and filtered . The vials were flushed with filtrate. The samples were acidified with 3 droplets of 50 % v/v nitric acid . As dry weight was not determined in the eggs and larvae collected for ICP-MS analyses, concentrations are expressed as ng/g individual, and/or were recalculated using average dry weights determined in other individuals . For Cu uptake in eggs and larvae we calculated the limit of quantification (LOQ) to 0.05 ng/egg or larvae .Copper concentrations were further determined in eggs with developing embryos and hatched larvae. Before the start of the exposure, 6 replicates of 30 eggs (n = 30) were sampled to determine Cu concentrations before exposure onset. Further, samples (n = 30 eggs) were collected from 6 replicates of each exposure concentration at 96 h after the start of the exposure, while hatched larvae (n = 30 from 6 replicates per exposure) were sampled at 3 dph. The samples were kept frozen in polypropylene (PP) cryo-vials at −20 °C until preparation and analyses. After thawing, 1.5 mL of nitric acid  was added. The samples were digested in a heating chamber for 1 h at 60 °C, then for 2 h in 105 °C. After digestion, the samples were diluted with purified water to a final volume of 15 mL in metal free PP tubes. Final analyses were performed using an inductively coupled plasma triple quadrupole mass spectrometry instrument  with 2.3The number of eggs with dead embryos, hatched larvae, and dead larvae was assessed daily, and dead individuals and eggs shells of hatched larvae were removed. Hatched larvae were transferred to acid-washed glass beakers containing the same Cu exposure concentrations  and were held at the same temperature and light regime as the exposure containers. Larvae collected at different days were held separately and mortality in the groups was evaluated daily. Oxygen levels were assessed daily as described above, and the exposure solutions were partly exchanged to ensure comparable water quality to the main exposure containers.2.42 -value of 0.9) corrected for temperature, air pressure and the number of individuals (n = 6) in the measurement chamber.Oxygen consumption in both eggs with developing embryos as well as hatched larvae was measured in a micro-respirometer  with a volume of 940 μL per chamber. Two replicates per treatment were measured daily, each replicate containing six embryos or larvae. Mean oxygen consumption per individual was calculated based on the linear decline of oxygen concentration  were sampled daily, embedded in a methylcellulose gel, and imaged with a macroscope  using a CMOS camera . Larvae were ranked (using anonymised numbers) for the occurrence of spine-, craniofacial-, jaw-, tail-, marginal finfold- and heart deformations, as well as alterations in pigmentation (lack of - or little pigmentation). The severity was ranked similarly to previous studies ,19 usingTo determine impacts on larvae growth and development, biometric traits  were automatically determined using the MASK-R CNN neural net architecture as decribed in more detail in  was used for data analyses. Data sets were analysed for normality  and analysed with either one-way ANOVA or Kruskal-Wallis tests and associated post-hoc tests to determine differences between groups.To determine mortality of eggs/hatching success, we used an approach of Kotani et al.  to corre2.8The model used was the DEBkiss model for embryos, as explained in detail in Jager et al. . In thatyA) and length of the yolk sac (yL) were used to calculate yolk volume (yV), using the relationship for a prolate spheroid as shown in equation 1:The projected 2D area (sV) was estimated from the approximate area of the structural body  and the myotome length (sL), assuming the relationship for a cylinder (equation 2):Structural volume  and total wet weight (pre hatch only). The state variables in the model are the dry masses of yolk and structure. Structural volume was calculated from structural mass using the dry-weight density t = 0. This is consistent with the increase in perivitelline space over the initial development of the egg [Few model modifications were used to model these additional endpoints, relating to specific deviations from the model predictions. Since the embryo is metabolising yolk to fuel its maintenance and development, as evident from the use of oxygen, a decrease in dry weight is unavoidable. However, the total wet weight of the egg did not decrease over development. The logical conclusion is that the water content of the egg increases over development. It is possible that water is taken up in the egg to replace the biovolume of yolk that is burned. In the model, the wet weight was therefore kept constant over the egg stages at the value for  the egg , althoug the egg ), so it Vd , and T is the actual profile of measured temperatures in the test (interpolated with a cubic spline). All temperatures in this equation are entered in Kelvin.We fitted the same parameters as in Jager et al.  and compstant in , using rs et al. ), Tref iHatching time was set at 10.5 dpf, as most eggs hatched between 10 and 11 dpf in this study (see below). In the model, hatching time is only used for the dry-weight and wet-weight model output to simulate shedding of the chorion and the contents of the perivitelline space. The egg incubation temperature was around 9 °C, and using the Arrhenius relationship, that would imply a hatching time of 17.5 dpf for the reference temperature of 6 °C. This is quite consistent with the hatching time of approximately 16 days used in the previous study .s was added to the model, in DEBtox style  certainly do not allow quantification of the time pattern of effect. Therefore, we used a constant stress factor here. This stress factor applied to either the assimilation or maintenance process in a linear fashion as in equations 5 and 6:To test whether there is a dose-related effect of Cu, a stress factor x style . The model was fitted using likelihood optimisation, where the likelihood was calculated assuming independent normal distributions both across time and across endpoints for the residuals after square-root transformation. Robust confidence intervals were derived by profiling of the likelihood function.The model was implemented in the BYOM platform under Matlab . Oxygen levels remained satisfactory throughout the experiment, with typical oxygen levels in the exposure containers of 8.9 ± 0.1 ppm.Average determined Cu concentrations were < LOD for ctrl (LOD of 0.6 μg/L), and 0.93 ± 0.19 μg/L, 3.54 ± 1.56 μg/L and 6.51 ± 1.74 μg/L for low, medium, and high exposure groups, respectively. Measured Cu concentrations were close to nominal exposure concentrations at the start of the experiment with < LOD in ctrl, and 0.82 ± 0.05, 2.2 ± 0.1 and 5.6 ± 0.18 μg/L in low, medium and high exposures, respectively. Concentrations increased slightly during the experiment and reached 1.2 ± 0.22, 1.3 ± 0.09, 5.16 ± 0.3 and 9.5 ± 3.4 μg/L at the last sampling point.2 = 0.917; 2 = 0.004; Results show that cod eggs accumulated Cu in an exposure dependent manner A. Copper3.2Hatching time was relatively similar between exposure groups, with high and medium exposure groups hatching marginally earlier than ctrl and low groups A. Most lDespite the Cu accumulation in the eggs with the developing embryos, we did not observe effects on survival at the tested concentrations, exposure timepoints (exposure start at 4 dpf) and environmental conditions (temperature and light) as exposure to Cu did not affect mortality of the developing embryos A. For th3.3Clarias gariepinus), spinal deformation  or curved tails  in exposed eggs with developing embryos [Exposure to Cu did not induce a significant increase in deformations in larvae of Atlantic cod in this study. In the ctrl group, 18.3 % of all imaged larvae had some deformation or had low or lacking pigmentation. This was similar for larvae in the Cu exposed groups, with percentage of larvae that had 1 or more deformations being 21, 16.4 and 20.5 % in low, medium and high exposures, respectively. The relative amounts of the most frequently detected deformations are shown in  embryos , however embryos . Wang et3.4Integration of the determined biometric traits and oxygen consumption showed that the measurements fitted well to the applied DEBkiss model . Althougb and z of equation 4 converge to values that produce s = 0). This supports the visual observation that there is no Cu stress on the determined growth traits followed in this study at the tested Cu concentrations.Adding the stress factor to the model did not improve the fit at all; the optimised log likelihood is same with and without the stress function  did not cause increased mortality, deformations or impacts on growth, development or energy budget in embryos and larvae upon exposure onset at 4 dpf to exposure termination at 5 dph. While acute and sublethal effects in the literature are reported for higher Cu concentrations (>50 μg/L) in early life stages of marine fish, it is noteworthy that lower effect concentrations are described for embryos exposed immediately after fertilisation, or larvae at later live stages.Julia Farkas: Conceptualization, Validation, Investigation, Formal analyses, Writing original draft, Writing review and editing, Visualization, Supervision, Project administration, Funding acquisition. Linn H. Svendheim: Validation, Investigation, Formal analyses. Tjalling Jager: Conceptualization, Methodology, Formal analyses, Writing original draft. Tomasz M. Ciesielski: Investigation, Formal analyses, Writing review and editing. Trond Nordtug: Methodology, Investigation. Bjarne Kvæstad: Methodology, Software. Bjørn H. Hansen: Conceptualization, Investigation, Supervision, Writing review and editing. Torstein Kristensen: Conceptualization, Writing review and editing. Dag Altin: Investigation, Writing review and editing. Pål A. Olsvik: Project administration, Funding acquisition, Supervision, Conceptualization, Writing original draft, Writing review and editing.The authors declare no conflict of interest.No data was used for the research described in the article.Data will be made available on request.All data is within the manuscript and figure.The authors report no declarations of interest.


Blood‐based indicators are potentially economical and a safe method for screening a population for dementia, although their predictive values have not been unequivocally confirmed. The present study proposes a dementia prediction formula based on serum indicators and patient characteristics.From January 2016 to December 2018, the data of elderly patients older than 60 years admitted to the Department of Neurology and Geriatrics in our hospital were retrospectively reviewed. A multivariate logistic regression model was applied to verify the patients’ characteristics and serum indicators associated with the risk of dementia. After receiver‐operating characteristic (ROC) curve and area under the ROC curve (AUC) analyses, we propose a dementia prediction formula and cutoff values for the predictive ability of early dementia.p < .001) and homocysteine (HCY) concentrations  were proven to be linked to the risk of developing dementia, while total cholesterol (TC)  was a protective factor for dementia. We developed a formula of age + low‐density lipoprotein cholesterol (LDL‐C) + TC + HCY + number of comorbidities as a good predictor of dementia (AUC: 0.79), with a probability (cutoff) value of 0.112 .Four thousand seven hundred twenty two elderly patients were enrolled, and the incidence of dementia was 12.0% (565). When patients had ≥8 comorbidities, their risk of developing dementia was 20 times higher than those without comorbidities. After multivariate regression analysis, age (OR: 1.086, High‐serum HCY and low TC were risk factors for developing dementia. A cutoff value > 0.112 derived from our formula was an excellent predictor for people at a high risk of developing dementia, and may be a potentially useful diagnostic tool for identifying patients at risk for dementia in routine clinical practice.   Ninety‐three percent of patients with dementia in the community have not been identified (33% higher than in the United Kingdom), and the standardized treatment rate is only 21.3% (less than one third of the United States), which means that the overall level of dementia diagnosis and treatment in China lags well behind high‐income countries  or admitted for hospitalization and treatment due to acute illness and were subsequently diagnosed with dementia (70%) or have been diagnosed with dementia and the condition aggravated to moderate and severe (10%).The control patients received therapy for a number of other conditions. Exclusion criteria were individuals with liver failure, a serum creatinine concentration >120 μmol/L, hyperthyroidism or hypothyroidism, disorders of the immune system, or on drugs that alter cholesterol, low‐density lipoprotein cholesterol (LDL‐C), and high‐density lipoprotein cholesterol (HDL‐C) concentrations. The diagnosis of dementia was made according to the 2018 Chinese guidelines for diagnosis and management of dementia and cognitive impairment (I): dementia classification and diagnostic ; HbA1C (mg/dL); total cholesterol, TC (mmol/L); LDL‐C (mmol/L); HDL‐C (mmol/L); HCY (μmol/L); folic acid (mmol/L); vitamin D2 (mmol/L); and vitamin D3 (mmol/L)). Cardiac disease was defined as a history of congestive heart failure; myocardial infarction; angina pectoris or medication with digitalis at any time; hypertension, systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg; use of antihypertensive agents. Diabetes refers to fasting glucose concentration ≥7 mmol/L, postprandial glucose or a 2 h 75 g oral glucose postloaded level ≥1 mmol/L, insulin, or oral hypoglycemic medication. Hypercholesterolemia refers to fasting TC ≥ 6.2 mmol/L or the use of lipid‐lowering drugs . Respiratory disorders refer to asthma, COPD, bronchiectasis, pulmonary fibrosis, or sarcoidosis. Cerebrovascular disease refers to stroke, transient ischemic attacks, aneurysm, or vascular malformations. The history of gastrointestinal disorders refers to the esophagus, stomach, small intestine, large intestine, rectum, pancreas, gallbladder, and liver. Nephrosis was defined as a urine test positive for protein, a blood test positive for protein levels lower than normal, and the clinical detection of edema. A history of fracture was defined as a break in any bone or cartilage. Tumor was defined as having a previous medical history of tumors.2.3Fasting venous blood samples (3 and 4 mL) were collected into tubes containing ethylenediaminetetraacetic acid (EDTA)‐K2 anticoagulant and vacuum‐separated gel blood collection vessels, respectively, and stored at −80°C for subsequent testing. FPG, TC, HDL‐C, LDL‐C, HCY, folic acid, vitamin D2, and vitamin D3 concentrations were measured with an Advia Clinical Chemistry System .2.4p‐value <.05.SPSS version 23  was employed to analyze all datasets. Discrete data are given as numbers or percentages and continuous data with a normal distribution as the mean ± SD. To analyze potential risk factors affecting dementia, uni‐ and multivariate logistic regression was employed. Data are given with 95% confidence intervals. The predictive ability of indicators for dementia was evaluated by receiver operating characteristic (ROC) analysis. The cutoff values for indicators were determined by ROC analyses (Youden Index). Variables with statistical significance in the univariate analysis were combined in different ways, to judge the diagnostic effect (ROC) of different combinations. Finally, the optimal combination (the largest area under the ROC curve) was recommended according to the fitting efficiency of different combination models. A statistically significant finding was deemed to be a two‐sided 33.1A total of 4722 elderly patients were included, with an average age of 73.0 ± 15.5 years, and 52.5% were males. Most of the patients were in the Department of Neurology (77.8%). There were 565 patients with dementia, with an incidence rate of 12%. Cerebrovascular disorders, hypertension, and cardiac disorders were the top three comorbidities, accounting for 74.2%, 59.5%, and 38.9%, respectively (Table 3.2p < .001), fractures , cardiac disorders , hypertension , and cerebrovascular disorders  were associated with a higher risk for the incidence of dementia. However, diabetes did not increase the risk of developing dementia. From the perspective of the number of comorbidities, OR increased with the number of comorbidities. When patients had ≥8 comorbidities, their risk of developing dementia was 20 times higher than those without comorbidities. Even two to three comorbidities increased the odds of dementia by a factor of 7.75. On the other hand, hyperlipidemia was the only indicator we found that was negatively linked to the dementia risk  , LDL‐C , and vitamin D3 , whereas it increased with higher concentrations of HCY   and HCY concentrations  were risk factors for developing dementia, while TC  was a protective factor against developing this condition  and found that age + LDL‐C + TC + HCY + number of comorbidities was a good predictor of dementia (AUC: 0.79), with a cutoff value of 0.112  /(1 + exp (−10.2858 + 0.1074 × age + 0.3922 × LDL‐C − 0.3901 × TC + 0.0113 × HCY + 0.0785 × number of comorbidities)) to be used to identify people who were at an increased risk of developing dementia. These results indicated that a combination of age + LDL‐C + TC + HCY + number of comorbidities may be a potential candidate formula to predict dementia.We developed a formula  as a predictive tool to screen out patients at a higher risk of developing dementia at the community level, thus providing the basis for further accurate diagnosis.Table As a result of the analysis of the general characteristics of patients, we found that age was a risk factor that was uncontrollable. Age was clearly the biggest risk factor for developing dementia, and most patients with sporadic dementia start to get ill after the age of 65. Epidemiological studies  burden (NAB) is a good predictor of the progress of AD. One study recommended predicted human NAB level measurements based on the molecular characteristics of blood  (Burnham et al., There are a number of limitations to our research that should be considered. First, the patients in our study were all Han people who live in Shanghai. Although this study analyzed a large cohort of patients, caution is needed when extending our conclusions to people of other races and cities. Second, we made no comparisons between the different clinical types and different levels of cognitive impairment of dementia. Third, there may be a reverse causal relationship between lipid levels and dementia, and patients with dementia may be more likely to suffer from eating disorders and malnutrition, which may lead to lower cholesterol levels in the body. Unfortunately, the design of a cross‐sectional study makes it impossible to explore causality. Further prospective studies are needed to provide unequivocal evidence of causality.In conclusion, this real‐world cross‐sectional study of a large sample size found that high HCY concentrations and low TC concentrations were independent risk factors for dementia in elderly patients. The formula of age + LDLC + TC + HCY + number of comorbidities predicted dementia and may serve as a cost‐effective tool for its early detection in people at a risk of developing dementia, and who could benefit from further invasive or indeed expensive confirmatory tests.The authors declare that there is no conflict of interest.Study concept and design: All authors. Acquisition of data: Qing Gong, Minghui Bi, and Lina Yu. Analysis and interpretation of data: All authors. Drafting of the manuscript: Qing Gong, Minghui Bi, and Lina Yu. Critical revision of the manuscript for important intellectual content: Lianhong Xie.


Frailty is a kind of geriatric syndrome, which is very common in the elderly. Patients with malnutrition are at higher risk of frailty. This study explored the correlation between nutrition and frailty and compared the receiver operating characteristic curve of different nutritional indexes for frailty.This cross-sectional study included 179 inpatients aged ≥65 years old. Frailty was measured using Fried Frailty Phenotype, handgrip strength was measured using JAMAR@Plus and the 4.57 m usual gait speed was measured using a stopwatch. Comprehensive nutritional assessment refers to the application of Mini Nutritional Assessment (MNA) to assess the nutritional status of patients.P < 0.05). Comprehensive nutritional assessment, whether as a categorical variable or a continuous variable, was significantly correlated with frailty (P < 0.05). Model1 showed that the risk of frailty in malnourished patients was 3.381 times higher than that in well nourished patients (P = 0.036). Model2 showed that the risk of frailty decreased by 13.8% for every 1 point increase in MNA score (P = 0.009). The area under the curves of albumin, prealbumin and hemoglobin was larger (AUC > 0.65), AUC was 0.718, 0.693 and 0.743, respectively.Compared with the non-frailty group, the upper arm circumference, calf circumference, hemoglobin, albumin, prealbumin, cholesterol and low density lipoprotein in the frailty group were lower (Our results suggest that malnutrition is closely related to frailty. As for single nutritional indexes, albumin, prealbumin and hemoglobin were found to be associated with frailty. Further cohort studies are needed to verify their ability to screen for frailty. Frailty is undoubtedly one of the most serious global public health challenges we will confront in the coming century. The rapid expansion of the aging population has led to a corresponding increase in the number of frail elderly , 2. FraiNutrition is a crucial factor closely related to frailty syndrome . MalnutrThis work is a cross-sectional study conducted in the cadre ward of the First Hospital of Jilin University from December 2020 to June 2021. In patients aged 65 years or above were recruited. Patients who were unable to walk for neuromusculoskeletal diseases such as Parkinson’s disease, severe knee or hip osteoarthritis, lumbar spinal stenosis, and Participants with malignant tumors, severe cognitive impairment, autoimmune diseases, severe liver and kidney dysfunction were excluded from the study. The study was in line with the declaration of Helsinki Declaration and approved by the ethics committee of the First Hospital of Jilin University.Age, gender and exercise were evaluated by self-made face-to-face interview questionnaire.Clinical information comes from electronic medical records, including smoking history, drinking history, types and diseases of chronic diseases, hemoglobin, albumin, prealbumin, cholesterol, triglyceride and low density lipoprotein Cholesterol.Height and weight were measured according to clinical standards.Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared.Walking speed was calculated as 4.57 m walk time divided by 4.57 m , 10. ParHandgrip strength of the dominant hand was measured by handgrip dynamometer (JAMAR@Plus) for 3 times, and the maximum handgrip strength was taken for analysis , 12.Participants stood with their feet shoulder width apart and the calf circumference around the most prominent part of the gastrocnemius muscle was measured (accurate to 0.1 cm).Arm circumference at the level of the thickest part of biceps brachii muscle was measured with the upper limb sags naturally (accurate to 0.1 cm).2, handgrip strength ≤29 kg; 24.1 ≤ BMI ≤ 28.0 kg/m2, handgrip strength ≤30 kg; BMI > 28 kg/m2, handgrip strength ≤32 kg. Decreased handgrip strength of female: BMI ≤ 23.0 kg/m2, handgrip strength ≤17 kg; 23.1 ≤ BMI ≤ 26.0 kg/m2, handgrip strength ≤17.3 kg; 26.1 ≤ BMI ≤ 29.0 kg/m2, handgrip strength ≤18 kg; BMI > 29 kg/m2, handgrip strength ≤21 kg. Male < 383 kcal / week or female < 270 kcal / week was judged as physical activity decrease. Walking speed was calculated by the time required to walk 4.57 m, male with height ≤ 173 cm, walking speed ≥7 s or height > 173 cm, walking speed ≥6 s was identified as walking speed descent; while the standard of walking speed descent among female is height ≤ 159 cm, walking speed ≥7 s or height > 159 cm, walking speed ≥6 s.Frailty was defined as a major biological phenotype by Fried Frailty Phenotype . SatisfyMini Nutritional Assessment (MNA) is a nutritional assessment tool with 18 questions specially developed for the elderly. It consists of four parts: anthropometry , self-reported health status, dietary problems (including weight loss) and clinical health status. MNA was used to comprehensively evaluate the nutritional status of elderly patients , 15. Thep < 0.05 were considered statistically significant.SPSS/WIN 23.0 software  or MedCalc 18.2.1  was used for statistical analysis. Kolmogorov-Smirnov test was used to test the normality of continuous variables. Continuous variables were presented as mean ± standard deviation or median (IQR) based on the distribution characteristics of the data. Student’s t-test or Mann-Whitney U test was used to test the difference between two groups. Classified variables were described as absolute number and percentages, and the difference between two groups was test by Chi-squared test. Multivariable logistic regression model was used to explore the association between malnutrition and frailty. The AUC value was calculated by drawing ROC curve to identify the association between different nutritional indexes and senile frailty. The Youden index was used to determine the optimal cutoff point. Comparison of two ROC curves was based on the method of Delong et al. . Values P < 0.001). In addition, there were significant differences in the proportion of smoking and exercise. The difference in the number of chronic diseases, activities of daily living score, MNA score and nutritional status between the two groups were also exhibit statistically significant (P < 0.05).As shown in Table P < 0.05).As shown in Table P < 0.05). Model1 showed that the risk of frailty in malnourished patients was 3.381 times higher than well-nourished patients (P = 0.036). Model2 showed that the risk of frailty decreased by 13.8% for every 1 point increase in MNA score (P = 0.009).As shown in Table The correlation between different nutritional indexes on the occurrence of frailty in elderly was analyzed and compared. Results are shown in Table In addition to confirming previous reports, the advantage of our research lies in a more comprehensive analysis of the correlation between different nutritional indicators , laboratory related indicators) and frailty, and further study and explore the relationship between them by analyzing the receiver operating characteristic curve of different nutritional indexes. For a single nutritional index, our study found that albumin, prealbumin and hemoglobin are related to frailty, which provides a clue for screening frailty in the future. A review on frailty and malnutrition in 2016 supports this conclusion and points out that both malnutrition and frailty are reversible and the incorrectly identification can lead to the onset of disability. Nutritional therapy, protein rich diet, correction of vitamin D deficiency and physical activity are all basic aspects of multidisciplinary treatment of senile frailty . A recenP < 0.05). Smoking is a risk factor for atherosclerosis [Our study also found that there were significant differences in the proportion of smoking and exercise, the number of chronic diseases, the ADL score, MNA score and nutritional status between the frailty group and the non-frailty group , gene and environment , energy metabolism changes caused by chronic diseases , stress response injury , chronic inflammation, HPA axis, autonomic nervous system, decreased neural function, decrease of anabolic hormones, eventually leading to physical weakness . The accThere are several limitations in current study. The main limitation is that this study is a cross-sectional design, so there is no statement of causality. Another limitation is the study samples are from one medical center, so the results are limited in universality. In conclusion, more prospective cohort studies in the elderly are needed to further understand the potential role of nutrition in preventing, delaying or reversing frailty syndrome.Our results suggest that malnutrition is closely related to frailty. As for single nutritional indexes, albumin, prealbumin and hemoglobin were found to be associated with frailty. Further cohort studies are needed to verify their ability to screen for frailty.


This study aimed to compare the rates of major cardiovascular adverse events in sodium‐glucose cotransporter‐2 inhibitors (SGLT2I) and dipeptidyl peptidase‐4 inhibitors (DPP4I) users in a Chinese population. SGLT2I and DPP4I are increasingly prescribed for type 2 diabetes mellitus patients. However, few population‐based studies are comparing their effects on incident heart failure or myocardial infarction.P < 0.0001), myocardial infarction , cardiovascular mortality , and all‐cause mortality  after adjusting for significant demographics, past comorbidities, and non‐SGLT2I/DPP4I medications.This was a population‐based retrospective cohort study using the electronic health record database in Hong Kong, including type 2 diabetes mellitus patients receiving either SGLT2I or DPP4I from 1 January 2015 to 31 December 2020. Propensity score matching was performed in a 1:1 ratio based on demographics, past comorbidities, and non‐SGLT2I/DPP4I medications with nearest neighbour matching . Univariable and multivariable Cox models were used to identify significant predictors for new‐onset heart failure, new‐onset myocardial infarction, cardiovascular mortality, and all‐cause mortality. Sensitivity analyses with competing risk models and multiple propensity score matching approaches were conducted. A total of 41 994 patients  were included with a median follow‐up of 5.6 years (IQR: 5.32–5.82). In the matched cohort, SGLT2I use was significantly associated with lower risks of new‐onset heart failure (hazard ratio [HR]: 0.73, 95% confidence interval [CI]: , SGLT2 inhibitors are protective against adverse cardiovascular events including new‐onset heart failure, myocardial infarction, cardiovascular mortality, and all‐cause mortality. The prescription of SGLT2I is preferred when taken into consideration individual cardiovascular and metabolic risk profiles in addition to drug–drug interactions. Diabetes mellitus is an increasingly prevalent metabolic disease, currently affecting more than 400 million people, and the patient population is projected to increase up to 642 million by 2040.et al. have demonstrated lower mortality in SGLT2I users in comparison with DPP4I users in a network meta‐analysis.Whilst small‐scale trials are comparing the metabolic effects or specific disease outcomes of SGLT2I and DPP4I, there is a lack of large‐scale population studies to evaluate the difference in the presentation of major cardiovascular adverse events between the use of the two drug classes.This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster and from The Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee. It included type 2 diabetes mellitus patients with SGLT2I or DPP4I prescriptions from 1 January 2015 to 31 December 2020. Patients who received both DPP4I and SGLT2I, in addition to patients who discontinued the medication during the study, were excluded. The exclusion criteria for the HF study cohort were as follows: patients with prior HF diagnosis or with the use of medications for HF (e.g. diuretics for HF and beta‐blockers for HF). For the MI study cohort, patients with prior old MI or MI diagnosis were excluded. The patients were identified from the Clinical Data Analysis and Reporting System (CDARS), a territory‐wide database that centralizes patient information from individual local hospitals to establish comprehensive medical data, including clinical characteristics, disease diagnosis, laboratory results, and drug treatment details. The system has been previously used by both our team and other teams in Hong Kong to conduct population‐based cohort studies,International Classification of Diseases Tenth Edition (ICD‐10) coding, whilst the study outcomes and comorbidities were documented in CDARS under ICD‐9 codes. The ICD codes used to search for diagnoses and outcomes are shown in SupporClinical and biochemical data were extracted from CDARS for the present study. Patients' demographics include sex and age of initial drug use (baseline). Prior comorbidities before initial drug use were extracted, including diabetes with chronic complication, diabetes without chronic complication, gout, hypertension, ischaemic heart disease, liver diseases, peripheral vascular disease, renal diseases, stroke/transient ischaemic attack, atrial fibrillation, ventricular tachycardia (VT)/ventricular fibrillation (VF)/aborted sudden cardiac death (SCD), anaemia, overweight, and cancer. Charlson's standard comorbidity index was also calculated. Mortality was recorded using the Non‐SGLT2I/DPP4I medications were also extracted, including metformin, sulphonylurea, insulin, acarbose, thiazolidinedione, glucagon‐like peptide‐1 receptor agonists, and statins and fibrates. A limited number of enrolled patients have been prescribed calcium channel blockers; thus, they were not considered. Baseline laboratory data were extracted. Subclinical biomarkers were calculated accordingly, including neutrophil‐to‐lymphocyte ratio, platelet‐to‐lymphocyte ratio, neutrophil‐to‐high‐density lipoprotein ratio, lymphocyte‐to‐high‐density lipoprotein ratio, lymphocyte‐to‐low‐density lipoprotein ratio, low‐density lipoprotein ratio‐to‐high‐density lipoprotein ratio, total cholesterol‐to‐high‐density lipoprotein ratio, triglyceride‐glucose index, bilirubin‐to‐albumin ratio, protein‐to‐creatinine ratio, and prognostic nutritional index.TableStandard deviation (SD) was calculated for glycaemic and lipid profile parameters once there are at least three examinations for each patient since initial drug exposure of SGLT2I or DPP4I. We also calculated more specific variability measures for HbA1c and fasting glucose profiles including SD, SD/initial, coefficient of variation (CV), and variability independent of mean as listed in Supporting Information, U test, whilst the two‐tailed χ2 test with Yates' correction was used to test 2 × 2 contingency data. Univariable Cox regression was used to identify significant predictors for the primary and secondary outcomes. Propensity score matching was performed to generate control of SGLT2I users to compare against DPP4I users in a 1:1 ratio based on baseline age, sex, prior comorbidities, and non‐SGLT2I/DPP4I medications using nearest neighbour matching strategy.The study outcomes are new‐onset HF, and new‐onset MI, cardiovascular mortality, and all‐cause mortality as defined by the first incidence of ICD‐9 codes of these adverse events SupporTable S1P‐value were reported. Statistical significance is defined as P‐value < 0.05. The statistical analysis was performed with RStudio software (Version 1.1.456) and Python (Version 3.6).Multivariable Cox models adjusting for significant risk factors of demographics, past comorbidities, non‐SGLT2I/DPP4I medications, subclinical biomarkers, HbA1c, and fasting glucose to identify the treatment effects of SGLT2I vs. DPP4I on the mentioned adverse outcomes. Cause‐specific and subdistribution hazard models were conducted to consider possible competing risks. Lastly, subgroup analyses were done on age (≤65 and >65 years) and sex on drug exposure effects. A standardized mean difference (SMD) of no <0.2 between the treatment groups post‐weighting was considered negligible. The hazard ratio (HR), 95% CI, and TableFigureIn this study, patients with type 2 diabetes mellitus and use of either SGLT2I or DPP4I from 1 January 2015 to 31 December 2020 were included Table1. PatiFigureTablesP < 0.0001), new‐onset MI , cardiovascular mortality , and all‐cause mortality . Multivariable Cox models were developed adjusting for significant demographics, past comorbidities, and medications (TableP < 0.001).The cumulative incidence curves for new‐onset HF, MI, cardiovascular mortality, and all‐cause mortality stratified by SGLT2I or DPP4I use for the matched cohort are shown in TableTableTableTo evaluate the predictiveness of the models, different sensitivity analyses were performed. Firstly, a 1 year lag time between treatment initiation and study outcomes was applied (Supporting Information, The main finding of the present study is that using DPP4I as a reference, SGLT2I use was associated with a lower risk of new‐onset HF and MI, cardiovascular mortality, and all‐cause mortality.Our findings are largely consistent with existing studies. A network meta‐analysis of 236 trials has reported the superior cardiovascular protective effects of SGLT2I against DPP4I when users of either medication are compared against the control group. However, the control groups were not matched and no direct comparison was made.There are several hypotheses for the mechanisms underlying the cardiovascular‐protective effects of SGLT2I. First of all, the modulatory effect of SGLT2I on the proximal tubules results in glucosuria and natriuresis, thus lowering the preload and the resulting stress on the ventricles.Moreover, inhibition of the sodium‐hydrogen ion exchanger in the myocardium, which is activated under HF to increase intracytoplasmic sodium and calcium level, was also hypothesized to be a part of the underlying mechanism.The multiple processes involved in the cardiovascular‐protective effect of SGLTI may also explain its superior outcome against DPP4I. Whilst previous studies reported the benefits of SGLT2I on cardiovascular health are mainly attributed to its protection against HF, a recent territory‐wide study has shown that SGLT2I users have a lower incidence of new‐onset atrial fibrillation than DPP4I users, which supports the lower cardiovascular and all‐cause mortality reported in the present study.There are several limitations to the present study. Firstly, inherent information bias with a risk of under‐coding and coding errors should be noted, given its observational and retrospective nature. However, the difference in patient characteristics, past comorbidities, and other medication usages between SGLT4I/DPP4I users and controls was addressed through matching using propensity scores, although residual bias may remain. There are also patients with missing data for the laboratory parameters because not all blood tests were routinely performed for all. Moreover, we were unable to access important lifestyle predictors for cardiovascular adverse events, such as body mass index, smoking, and alcoholism. Thirdly, coding for clinical diagnoses of HF was used but echocardiographic data are not coded in the administrative database, and therefore, different types of HF based on ejection fraction could not be examined. Finally, DPP4I use is associated with an increased risk of HF compared with placebo, and therefore, this study could not distinguish between whether gliptins cause HF and whether SGLT2I reduce HF.SGLT2 inhibitors are protective against adverse cardiovascular events including new‐onset HF, MI, cardiovascular mortality, and all‐cause mortality. The prescription of SGLT2I is preferred when taken into consideration individual cardiovascular and metabolic risk profiles in addition to drug–drug interactions.None declared.None.Jiandong Zhou and Sharen Lee: conception of study and literature search, preparation of figures, study design, data collection, data contribution, statistical analysis, data interpretation, manuscript drafting, and critical revision of the manuscript.Keith Sai Kit Leung, Abraham Ka Chung Wai, Tong Liu, Ying Liu, Dong Chang, Wing Tak Wong, Ian Chi Kei Wong, and Bernard Man Yung Cheung: conception of study and literature search, data collection, data contribution, critical revision of the manuscript, and study supervision.Qingpeng Zhang and Gary Tse: conception of study and literature search, study design, data collection, data analysis, data contribution, manuscript drafting, critical revision of manuscript, and study supervision.Figure S1. Propensity score matching comparisons for SGLT2I v.s. DPP4I before and after 1:1 matching with nearest neighbor search strategy using a caliper of 0.1.Table S1. ICD‐9 codes for diagnoses and ICD‐10 codes for outcomes.Table S2. Univariable Cox regression to identify significant predictors of heart failure and myocardial infarction before and after 1:1 matching.P ≤ 0.05, ** for P ≤ 0.01, *** for P ≤ 0.001; HR: hazard ratio; CI: confidence interval; SD: standard deviation; SCD: sudden cardiac death; VF: ventricular fibrillation; VT: ventricular tachycardia; SGLT2I: sodium glucose cotransporter‐2 inhibitor; DPP4I: dipeptidyl peptidase‐4 inhibitor; CV: coefficient of variation.* for Table S3. Univariable Cox regression to identify significant predictors of cardiovascular mortality and all‐cause mortality before and after 1:1 matching.P ≤ 0.05, ** for P ≤ 0.01, *** for P ≤ 0.001; HR: hazard ratio; CI: confidence interval; SD: standard deviation; SCD: sudden cardiac death; VF: ventricular fibrillation; VT: ventricular tachycardia; SGLT2I: sodium glucose cotransporter‐2 inhibitor; DPP4I: dipeptidyl peptidase‐4 inhibitor; CV: coefficient of variation.* for Table S4. Sensitivity analysis 1: Multivariable Cox models with a one‐year lag time.Table S5. Sensitivity analysis 2: Competing risk analyses.Table S6. Sensitivity analysis 3: Different propensity score approaches.Click here for additional data file.


Nonalcoholic fatty liver disease (NAFLD) refers to excess fat accumulation in the liver. In animal experiments and human kinetic study, we found that administration of combined metabolic activators (CMAs) promotes the oxidation of fat, attenuates the resulting oxidative stress, activates mitochondria, and eventually removes excess fat from the liver. Here, we tested the safety and efficacy of CMA in NAFLD patients in a placebo‐controlled 10‐week study. We found that CMA significantly decreased hepatic steatosis and levels of aspartate aminotransferase, alanine aminotransferase, uric acid, and creatinine, whereas found no differences on these variables in the placebo group after adjustment for weight loss. By integrating clinical data with plasma metabolomics and inflammatory proteomics as well as oral and gut metagenomic data, we revealed the underlying molecular mechanisms associated with the reduced hepatic fat and inflammation in NAFLD patients and identified the key players involved in the host–microbiome interactions. In conclusion, we showed that CMA can be used to develop a pharmacological treatment strategy in NAFLD patients. A placebo‐controlled human study shows that oral administration of Combined Metabolic Activators (CMA) reduces liver fat in nonalcoholic fatty liver disease (NAFLD) patients. Similar5 Fig . S‐05) Fig .P.adj = 0.003) and Day 70 , both before and after weight loss adjustment , both before and after weight loss adjustment , whereas in the placebo group, it was unchanged and diastolic blood pressure had increased (P = 0.038) ; of N‐acetylglycine, N‐palmitoylserine, N‐oleoylserine, and N‐stearoylserine (associated with serine and glycine metabolism); and of deoxycarnitine, acetylcarnitine, and butyrylcarnitine (associated with carnitine metabolism) were significantly higher on Day 70 than on Day 0 in the CMA group (Dataset P = 0.052) to be downregulated on Day 70 versus Day 0 in the CMA group  in the CMA group; 44 metabolites were involved in lipid metabolism  using an inflammation panel quantifying the plasma level of target proteins. After quality control and exclusion of proteins with missing values in more than 50% of samples, 72 proteins were analyzed Dataset . Proteinet␣al, et␣al, et␣al, et␣al, et␣al, The plasma level of FGF‐21 is increased in NAFLD patients and is a potential diagnostic marker of NAFLD , Actinobacteria , and Firmicutes  were significantly decreased. However, in the placebo group species belong to Firmicutes (Firmicutes bacterium CAG 83 and Oscillibacter sp 57 20) and Bacteroides vulgatus were significantly reduced  was significantly increased in the CMA group , Bacteroidetes (Prevotella maculosa), and Actinobacteria  was significantly decreased in the CMA group  and Bacteroidetes (Bacteroides ovatus and Bacteroides fragilis). Additionally, we observed that the plasma levels of N1‐methyl‐4‐pyridone‐3‐carboxamide and N1‐methyl‐2‐pyridone‐5‐carboxamide are significantly positively correlated with the abundance of Alistipes shahii and negatively correlated with the abundance of Bacteroides cellulosilyticus and Fusicatenibacter saccharivorans  and various metabolites Fig . The plaWe assessed the associations between improved clinical parameters and inflammation by identifying the significant correlations between improved clinical variables  and various inflammation‐related proteins Fig . We obsePrevotella copri in gut microbiome were positively correlated with liver fat, AST, ALT, and systolic blood pressure level , an open‐access interactive web platform for multi‐omics data visualization and database  can be viewed at iNetModels (Roseburia faecis (significantly downregulated in the CMA group) is significantly negatively correlated with the plasma level of serine and carnitine  reported only mild‐to‐moderate adverse events, including nonspecific gastrointestinal (33%) and musculoskeletal (20%) symptoms, and all patients decided to complete the study Dataset . GastroiBased on data‐driven modeling and systems biology analysis, we found that administration of CMA may increase the liver's fat oxidation, enhance the mitochondrial function in the cells, and reduce oxidative stress. To test this hypothesis in a clinical setting, we performed a randomized, placebo‐controlled phase 2 study to investigate the efficacy and safety of CMA in NAFLD patients. We observed improvements on clinical variables, plasma metabolomics, inflammatory proteomics, and oral/gut microbiome after CMA treatment in NAFLD patients Fig . We foun+ for mitochondrial activation. These metabolites have shown to be increased in a recent study, supporting the insulin‐sensitizing ability of the nicotinamide mononucleotide, another NAD+ intermediate, in skeletal muscle of prediabetic women , or butyrate producers  remained in the sample pool, which hinder the possibility to perform sex‐specific analysis. Second, patients who had type 2 diabetes or dyslipidemia or were taking anti‐diabetic medications were excluded, which resulted in a more homogenous clinical trial group. Third, hepatic fat content and inflammation were assessed by MRI‐based methods and plasma inflammatory protein markers rather than liver histology. Our findings warrant a clinical trial in patients with biopsy‐proven nonalcoholic steatohepatitis to delineate the effects CMA on hepatic injury and inflammation.et␣al, The safety profile of CMA in these patients was consistent with the results of our previous one‐day calibration study and clinical trials, including only a single component of the CMA . Patients were excluded if they carried the PNPLA3 I148 M (homozygous for I148 M), had ALT or AST levels > 3‐fold higher than the upper limit of normal, or had taken oral antidiabetics, including metformin, within 3 months. The inclusion and exclusion criteria are detailed in the Overweight or obese patients 18–70 years of age were enrolled in the trial if they were diagnosed with NAFLD and mPatients were randomly assigned to CMA or placebo (2:1). Patient information  was entered into the web‐based randomization system, and the randomization codes were entered into the electronic case report form.l‐carnitine tartrate, 1 g nicotinamide riboside, 12.35 g serine, and 2.55 g N‐acetyl‐l‐cysteine) daily for the first 14 days (after dinner) and two doses daily for the next 56 days (after breakfast and dinner). Further information is provided in the CMA treatment was given for 70 days after the initial diagnosis of high hepatic fat by MRI‐PDFF. Patients in the treatment group took one dose of CMA .In the plasma samples, inflammatory protein markers were determined with the Olink Inflammation panel  Datasets . Brieflyn (%). Safety and exploratory efficacy endpoints were analyzed in all randomized patients who received CMA. Differences in clinical and physiological variables between time points were analyzed by paired t‐test, followed by one‐way ANOVA to find shifts between CMA and placebo groups at each time point. Missing values were removed in a pairwise fashion.Values are expressed as the mean ± standard deviation (SD) (continuous variables) or as P < 0.05.For analysis of plasma metabolomics, raw data from Day 14 and Day 70 in both groups were scaled to data on Day 0. Metabolite changes in the CMA group versus the placebo group over time Dataset  were anaP < 0.05 was considered statistically significant.For analysis of plasma proteomics, raw data from Day 14 and Day 70 were scaled to data on Day 0. Subsequently, ANOVA was used to calculate the CMA group changes versus placebo over time Dataset . P < 0.0Finally, Spearman correlation analysis  was used to analyze the association between individual components of CMA and other metabolites Dataset  and betw® 3.0 Fluorometer  using the QubitTM dsDNA HS Assay Kit. DNA purity was determined via 260/280 and 260/230 ratios measured on the NanoDrop 1000 . The SMARTer Thruplex DNA‐Seq (Takara Bio) was used for library preparation . Samples were sequenced on NovaSeq6000(NovaSeq Control Software 1.7.0/RTA v3.4.4) with a 151nt (Read1)‐10nt(Index1)‐10nt(Index2)‐151nt(Read2) setup using “NovaSeqXp” workflow in “S4” mode flow cell. The Bcl to FastQ conversion was performed using bcl2fastq_v2.20.0.422 from the CASAVA software suite. The quality scale used is Sanger/phred33/Illumina 1.8+.Fresh stool and saliva specimens were collected and preserved using DNA/RNA Shield Fecal Collection Tubes  and DNA/RNA Shield Collection Tube , respectively. Saliva samples were collected during the visit; however, stool collection kits were provided to the patients and advised to collect stool samples in the last 24 h of the visit. DNA extractions from the fecal samples were done using QIAamp PowerFecal Pro DNA Kit  and the saliva samples using QIAamp DNA Microbiome Kit . All protocol procedures were performed according to the manufacturer's instructions. Quantification of extracted DNA was determined fluorometrically on the Qubitet␣al, SciPy package was used. All analyses were done using Python 3.Raw paired‐end metagenomic data were analyzed using Metaphlan3  are presented. The analyses were performed with the SciPy package in Python 3.7. Centrality analysis on the network was performed using iGraph Python.MU, JB, and AM designed the study; MZ, MA, BS, DU, and MGG recruited patients; HY, OA, MA, CF, WK, XL, JMS, CZ, SS, and JN did the experiments; HY, OA, MA, CF, WK, XL, JMS, CZ, SS, JN, and AM analyzed the data; MZ, MU, JB, and AM interpreted the data; MZ, OA, and AM wrote the manuscript; and all authors approved the final version of the manuscript.AM, JB, and MU are the founder and shareholders of ScandiBio Therapeutics and ScandiEdge Therapeutics. The other authors declare no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Dataset EV7Click here for additional data file.Dataset EV8Click here for additional data file.Dataset EV9Click here for additional data file.Dataset EV10Click here for additional data file.Dataset EV11Click here for additional data file.Dataset EV12Click here for additional data file.Dataset EV13Click here for additional data file.Dataset EV14Click here for additional data file.Dataset EV15Click here for additional data file.Dataset EV16Click here for additional data file.Dataset EV17Click here for additional data file.Dataset EV18Click here for additional data file.