For patients with large tumors palliative radiotherapy often is the only local treatment option. To prevent toxicity the administered doses are low. Dose escalation to the tumor could be an option to better smyptom control and prolong local control rates. In this prospective study we used a very pragmatic approach with a simultaneously integrated boost (SIB) to an almost geometrically defined tumor core to achieve this. The primary endpoint was to demonstrate feasibility.Patients with solid tumors > 4 cm in diameter of different histologies were eligible in this single arm, prospective, multi-institutional clinical feasibility trial with two treatment concepts: 5 × 5 Gy with an integrated boost to the tumor core of 5 × 10 Gy or 10 × 3 Gy with a boost of 10 × 6 Gy. The objective of dose escalation in this study was to deliver a minimum dose of 150% of the prescribed dose to the gross tumor volume (GTV) tumor core and to reach a maximum of at least 200% in the tumor core.3 (range 49.4–1179.6 cm3). The corresponding core volumes were 72.85 cm3 on average (range 4.21–338.3 cm3). Dose escalation to the tumor core with mean doses of 167.7–207.7% related to the nonboost prescribed isodose led to PTV mean doses of 120.5–163.3%. Treatment delivery and short-term follow-up was successful in all patients.In all, 21 patients at three study sites were recruited between January 2019 and November 2020 and were almost evenly spread (9 to 12) between the two concepts. The treated planning target volumes (PTV) averaged 389.42 cmPalliative radiotherapy with SIB to the tumor core seems to be a feasible and well-tolerated treatment concept for large tumors. The applied high doses of up to 50 Gy in 5 fractions (or 60 Gy in 10 fractions) did not cause unexpected side effects in the 42 day follow-up period. Further research is needed for more information on efficacy and long-term toxicity.The online version of this article (10.1007/s00066-022-01976-5) contains supplementary material, which is available to authorized users. Patients in a palliative setting are usually treated with the intent of rapid symptom control and low side effects. Therefore, radiotherapy is often the only suitable local treatment option. Typical doses and fractionation schedules are 5 fractions of 4–5 Gy or 10 fractions of 3 Gy, corresponding to a dose equivalent of 40 Gy or less in conventional fractionation . HoweverPalliative patients with large tumors often show symptoms like shortness of breath or pain. These patients are not good candidates for high-precision techniques which require either strict immobilization or are time-consuming; thus, the inclusion of a SIB for dose escalation should require standard techniques with robust plans. Whether optimization and administration of such plans in a wide spectrum of clinical situations is possible requires further investigation of optimal concepts.We report the results of a prospective feasibility study in which robust volumetric modulated arc therapy (VMAT) plans in standard settings were used to achieve the announced dose escalation in the palliative treatment of patients with large (> 4 cm) tumors in different clinical situations.The tumor core boost study was a single arm, prospective, multi-institutional clinical feasibility trial with a planned sample size of 25 patients treated with palliative intent for tumors (primary or metastases) larger than 4 cm in diameter. Treatment consisted of palliative radiotherapy with 30 Gy in 10 fractions or 25 Gy in 5 fractions. Which regimen to use was the investigator’s choice and depended on the general assessment of the patient’s overall clinical situation. The center of the target volume (the “tumor core”) received an additional boost dose and the dose to the core was escalated by 100% as compared to the standard prescription dose; thus, the integrated boost volume received a total dose of 60 Gy in 10 fractions or 50 Gy in 5 fractions. The aim of the study was to demonstrate that dose escalation with this SIB concept is feasible in a non-SBRT setting without high rates of side effects because of a significantly increased dose to surrounding organs at risk.Strahlenschutzverordnung or §28a Röntgenverordnung was needed.The study complied with the Declaration of Helsinki, and the protocol was approved by ethics committee of the Christian-Albrechts-University Kiel (D535/18) and listed on German Clinical Trials Register (DRKS00015763). As decided by DEGRO Expert Panel (inquiry 116), no approval analog §23 Patients were eligible if they had a large solid tumor (≥ 4 cm in diameter), almost irrespective of histology , and were scheduled for palliative radiotherapy to this lesion. Further inclusion criteria were age above 18 years and informed consent. Moreover, a tumor core of at least 2 cm in spherical diameter with a 1 cm margin in all directions inside the GTV was to be definable.Patients were excluded if critical normal tissue structures (CNTS) were inside the GTV  to avoid major complications due to overdosage to such critical volumes. Moreover, patients were excluded in case of pregnancy, high grade renal failure (glomerular filtration rate < 30 ml/min), and legal incapacity.The GTV included the whole macroscopic tumor as defined in the planning computed tomography (CT). Additional information from other imaging modalities like magnetic resonance imaging (MRI) or positron emission tomography CT (PET/CT) was also used, if available. The SIB volume, called GTV tumor core, was defined as a rotund volume measuring at least 20 mm in diameter and lying completely inside the GTV with an additional inner margin of at least 10 mm in all directions; this inner margin was chosen to safely avoid overdosage outside the GTV even in case of relatively larger motion during treatment. Further information on tumor biology  uptake in PET scanning or visible necrosis) was not considered for target volume definition in this study. Other macroscopic manifestations of tumor-like nodal metastases measuring < 4 cm could be included in the clinical target volume (CTV), but not in the high-dose GTV tumor core. The additional margin for the planning target volume (PTV) depended on the internal standards of the participating institutions.mean) and max dose (Dmax) were documented and compared with the doses of an additional standard radiotherapy (RT) plan with homogenous dose distribution without SIB. To simplify this plan comparison of the different OARs with the different fractionation regimes, we defined new patient-specific parameters: the maximum increase of any OAR’s Dmean and Dmax calculated in Gy and %.The listed OARs were to be contoured if they were within 5 cm in cranial-to-caudal orientation of the GTV: brain, pituitary gland, optic nerves, optic chiasm, eyes, lenses, parotid glands, spinal cord, lungs, heart, esophagus, liver, kidneys, bladder, rectum, bowel bag. For every OAR of every patient, mean dose , and Dmax to the PTV and GTV tumor core were documented. In addition, a standard plan with homogeneous dose distribution must be calculated for all patients; this could be, depending on the clinical situation, an appropriate VMAT, intensity modulated RT (IMRT) or a 3-dimensional conformal RT plan (3D-CRT).The prescribed dose was the dose on the PTV-surrounding isodose. The objective of dose escalation in this study was to deliver a minimum dose of 150% of the prescribed dose to the GTV tumor core and to reach a maximum of at least 200% in the tumor core. DRadiation was to be applied with daily image guidance via cone beam CT (CBCT) or stereoscopic X‑rays, if possible. Due to the palliative setting, exceptions were allowed for patients with special impairments. Overall treatment time (from the first to the last treatment fraction) should not exceed 3 weeks for patients receiving 10 fractions and 2 weeks for patients with 5 fractions.Patients were clinically investigated at baseline before start of radiation treatment (d0), weekly during radiotherapy at treatment days d1, d7, d14, and at the day of last treatment (RT+0d). Early follow-up visits at day RT+7d and day RT+14d were done via phone; at day RT+42d, all patients were clinically investigated. Because of the estimated short overall survival of the patients and the short follow-up period with focus on feasibility, an investigation of tumor response (RECIST criteria) was not performed.Optimization of treatment plans with SIB with protocol-defined maximum doses to the tumor core andSuccessful administration of the planned RT dose in at least 80% of patients.The primary endpoint of the study was feasibility of a SIB with 100% dose escalation to the tumor core in palliative RT. Feasibility criteria were defined asSecondary endpoints were toxicity and side-effects of radiotherapy with SIB. Side effects and SAEs (serious adverse event) were documented according to the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE) v4.03 . It was Recruitment of patients was lower than expected. We included a total of 21 patients from January 2019 until November 2020 until the study was closed at the end of 2020. All patients gave their informed consent. The patients were almost evenly spread over both fractionation regimes. Treated locations differed a lot. In 7 of 21 cases, tumors were located in the head and neck region, in 5 cases in the thoracic region , and in 9 cases the tumors were located in the abdomen or pelvis. In all, 7 primary tumors, 5 nodal metastases, and 10 distant metastases were treated. Non-small cell lung cancer (NSCLC) was the most common histology . Other histologies included endometrial cancer, head and neck squamous cell carcinoma (HNSCC), rectal cancer, melanoma, breast cancer, fallopian tube cancer, sarcoma, and squamous cell carcinoma of unknown primary (SSC-CUP). More patient characteristics are listed in Table The prescribed treatment was successfully delivered in all patients. More than 95% of the patients (20 of 21) completed treatment with SIB to the tumor core in the scheduled treatment time . Treatment of one patient in 5‑fractionation regime was completed within 17 days. No patient’s treatment was abandoned.Rates of acute toxicities at any point of the monitored period were 100% grade ≥ 1, 33.3% grade ≥ 2, and 9.5% grade ≥ 3, respectively. No grade 4 toxicities were observed. Corrected for toxicities documented at d0 or d1 , the estimated rate of acute toxicities grade ≥ 2 was 28.6%. Considering only those cases where the toxicities lasted until the last follow-up time-point (RT+42d), the rates were 61.1% for toxicities of grade ≥ 1 and 5.6% grade ≥ 2 (Table Six adverse events (AE) and three severe adverse events (SAE) were reported. Two SAEs resulted from unplanned hospitalization due to a systemic tumor progression and both patients died in the hospital; there was no evidence for radiation-related side effects. The third SAE was related to death of a patient during palliative care in a hospice. Before death, 2 of the 3 patients showed maximum documented acute toxicity grades of 1, while 1 patient showed grade 2. The reported AEs were moderate pneumonia , moderate local inflammation in the irradiated region , pancytopenia after chemotherapy requiring blood transfusion and granulocyte colony stimulating factor (G‑CSF), serious hemorrhage of a tumor before start of radiation treatment , moderate colitis after immunotherapy (treated with prednisolone), and repeated cardiac decompensations caused by known heart failure with delay of radiation treatment.The postulated feasibility requirements, namely the calculation of robust treatment plans with sparing of OARs and successful delivery of the prescribed treatment in more than 80% of the patients, were both met (treatment adherence 100%). With regard to the secondary endpoints, the appearance of toxicities greater than grade 2 on CTCAE scale was 28.6% and thus higher than the expected rate of 20%. However, the vast majority of the observed toxicity derived not from radiotherapy treatment but was rather caused by tumor-specific complications or related to other cancer-specific treatment, mainly chemotherapy.The objectives of dose escalation were a minimum dose to GTV tumor core of 150%  and a maximum dose of at least 200% of the prescript PTV enclosing isodose. These study objectives were reached in more than 90% of the patients. In two cases, minor protocol deviations with a slight underdosage of GTV tumor core were detected.In the main testing center  VMAT plans were generated with the treatment planning system Eclipse v.13.7.14  and the associated AAA algorithm for a TrueBeam STx linear accelerator (LINAC). VMAT techniques were used in all patients. In comparison with other modern technologies available in radiotherapy , VMAT is known to be more monitor unit (MU) efficient –19. Due At least half coplanar double arcs were used for all these plans. While using additional ‘shell’ structures, it was possible to modulate the dose gradient within the target volume. The aim was a preferably uniform dose fall from the integrated boost area in the core of the tumor to the target volume enclosing isodose. The number of shells used was based on the size and shape of the target volume.Normalization was performed typically on the maximum dose value, which was 50 Gy or 60 Gy, respectively, at the target maximum . To achieve a higher mean dose in the tumor core, it was also possible to normalize to the tumor core target mean . For the different normalization methods, the variability of several dose parameters  were then consecutively analyzed.3 (range 49.4–1179.6 cm3). This conformed to a mean idealized spherical diameter of 9.06 cm. The corresponding core volumes averaged 72.85 cm3 . The relation of core volume and PTV for all patients is shown in Fig. The treated PTVs had a mean of 389.42 cmMaximum doses to GTV tumor core and PTV were in all cases identical . At the main test center Kiel and in study site b , plans show a switch from maximum doses of 200% of the prescribed PTV dose to mean doses of exactly 200%. This resulted from the above-mentioned differences in plan normalization and led to higher maximum doses. In study site c , a third approach was used with significantly higher doses than at the other sites. The participating physicists focused on dose escalation as high as possible. First, they optimized the VMAT plans for around 200% dose maximum. Second, the resulting OAR doses were used and increased to obtain a higher dose maximum inside the GTV tumor core, while maintaining the prescript PTV surrounding isodose level. Treatment planning was performed with Monaco 5.10 . The different planning approaches are illustrated in Fig. The benefit of escalated PTV doses had one drawback—a significant increase of doses delivered to surrounding OARs. Altogether dose statistics of 136 delineated OARs were measured. Because the treated sites were located throughout the human body, not all to-be-documented OARs were delineated in every treatment plan. The spinal cord was the most common (delineated in 18 of 21 patients), optic chiasm the least (1 of 21). Doses to OARs in the tumor core boost plan were compared to doses in standard RT plan (without core boost).As an example, patient 21 (Tab. A1 supplement) with a PTV Dmax of 100.5 Gy was selected. Dmax to OAR spinal cord was 33.4 Gy in the core boost treatment plan and 11.1 Gy in the standard plan (both 10 fractions). This was an increase of 22.3 Gy or 200.9%. For another OAR (bowel bag) Dmax was 46.1 Gy (core boost), and 24.6 Gy . This was an increase of 21.5 Gy or 87.4%. Both parameters were higher for OAR spinal cord than for bowel bag and higher than for any other OAR of this patient. Consequently, 22.3 Gy and 200.9% were recorded for patient 21. Results of all patients are depicted in Fig. To our knowledge, an integrated boost to partial parts of large GTVs with the objective of radical dose escalation has not yet been investigated in prospective clinical trials although some case reports have described this approach , 22. TheThe primary endpoint of this feasibility study, a combination of technical planning criteria and treatment adherence, was met. Creation of treatment plans according to the protocol was possible in all patients and treatment adherence was excellent. Acute toxicity grade ≥ 3 was 28.6% and therefore slightly higher than the estimated 20% but none of the treated patients experienced radiotherapy-related severe toxicity; all severe adverse events were related to the underlying disease and were caused by tumor progression. Thus, this concept of inhomogeneous dose prescription to the GTV with a simultaneous integrated boost to the tumor core was feasible and well tolerated and might offer a tool for dose escalation in palliative treatment of large tumors.The study has on the other hand disclosed a variety of difficulties with this approach. Although the predefined protocol-specific dose-escalation concept could be adopted in all patients, plan statistics differed a lot. We achieved ablative radiation doses in the center of the GTV with significant increase in PTV mean dose (20–63%), even though sizes of the boost volumes were only 6–31% of the whole PTVs. As expected, relatively larger core volumes led to a higher increase of PTV mean dose. Due to the fact that the inner margin around the core boost volume was set to 1 cm for safety reasons , the boost volume in small tumors was relatively smaller compared to larger ones. To overcome this problem, smaller safety margins would be required for smaller tumors to increase the core volume to the relatively same size as in larger tumors. For example, in a spherical tumor with 5 cm in diameter, the core boost volume makes up only 22% of the whole GTV in case of a safety margin of 1 cm around the core. An increase of the core volume to more than half of the GTV would require reducing the safety margin to about 4 mm. Smaller margins like this require more precise image guidance (as used in SBRT) than were specified in this study .mean) might have no clinical effect in palliative situation and is consequently tolerable. On the other hand, one must be very careful with high absolute dose increases such as the outliers in Fig. max in 10 daily fractions compared to a well-established treatment regime (10 × 3 Gy) could lead to unpredictable toxicity. These extreme outliers were a consequence of the dose-maximizing approach of study center c. Another crucial impact to OAR statistics was the location of the treated tumor. Chan et al. had shown that dose escalation to lung tumors with an inhomogeneous dose distribution has no significant impact on the mean dose to the lung [Finally, the individual physical treatment planning also influences plan quality and the level of dose escalation a lot. The used and established techniques of the different planning systems  allow complex dose distributions. Each of the three planning systems used in this study had its advantages and the planning physicists had different ideas about how to deal with the study requirements and we did no central quality assessment of the different treatment plans. Study sites were responsible for their approaches. The use of additional shell structures in study site a might offer more influence in the arrangement of dose hotspots than the stricter dose-maximizing approach in study site c Fig. . The keythe lung . SubgrouFor more precise guidelines for treatment of large tumors, further studies are needed. To overcome the shortcomings of this study , an investigation with a much more homogenous study cohort  or different set-ups for different tumor sizes and locations is needed. In addition, more precise guidelines for GTV/PTV ratio, dose coverage, and dose gradients should be included. Finally, to validate clinical benefits of the concept different endpoints, more cases and consequently more participating institutes are required. Therefore, an additional multicenter study to implement the SIB concept in the treatment of large tumors is under development.Radical dose escalation with more than 50 Gy in 5 fractions or 60 Gy in 10 fractions to the tumor core was feasible in the small subgroup of palliative patients with large tumors. The delivery of the demonstrated SIB technique remains safe, and with use of large safety margins it is simple to implement in daily routine with standard treatment settings. Efficacy needs to be investigated in an additional trial.Table A1 in the supplementary material shows detailed characteristics of GTV tumor core and PTV for all patients treated with tumor core boost. The patient count is not chronological, instead it is sorted by treatment groups  and study sites . Deviations to protocol are marked fat.


N = 301, the Leipzig Study for Mind-Body-Emotion Interactions, N = 220), to replicate published findings and expand them by investigating diffusivity in the LC’s ascending noradrenergic bundle. In younger adults, LC fractional anisotropy was significantly lower, compared to older adults. However, in the LC’s ascending noradrenergic bundle, we observed significantly higher fractional anisotropy in younger adults, relative to older adults. These findings indicate that diffusivity in the LC versus the ascending noradrenergic bundle are both susceptible to structural changes in aging that have opposing effects on fractional anisotropy.The noradrenergic locus coeruleus (LC) is a small brainstem nucleus that promotes arousal and attention. Recent studies have examined the microstructural properties of the LC using diffusion-weighted magnetic resonance imaging and found unexpected age-related differences in fractional anisotropy - a measure of white matter integrity. Here, we used two datasets (Berlin Aging Study-II,  Fractional anisotropy is widely used as a measure of structural integrity  and has a strong inverse correlation with mean or radial diffusivity . BASE-II participants signed written informed consent and received monetary compensation for participation. MRI acquisitions were approved by the ethics committees of the German Psychological Society , for which extensive details can be found elsewhere . The Dec2.1.(v6.3) eddy-current and motion correction, brain extraction tool, and resampling to isotropic resolution of 2mm3 (v3.1) to compute fractional anisotropy (FA) and eigenvalue images  Pipeline, we applied FSL’s  of 2mm3 . We usede images . With die images  and the e images , which rMATLAB ver. R2019a). After accurate atlas registration to individual subject space was confirmed with visual inspection, mean and radial diffusion images were created from eigen value images in MATLAB with custom scripts. Atlases were then converted into a binary mask and multiplied by the diffusion image to provide fractional anisotropy, mean, and radial diffusivity values along the atlases, per voxel, within the native space. Diffusivity values were then averaged to provide one diffusivity value per participant within each ROI.Both fractional anisotropy and atlas images were registered into MNI152 linear, 1 mm brain space. Using ANTS nonlinear registration  the atlaSince the noradrenergic bundle overlaps with a portion of the LC atlas, we conducted an along-tract analysis of fractional anisotropy of the noradrenergic bundle. 50 equidistant points were imposed along the noradrenergic bundle as discussed elsewhere . Each po2.2.All statistical analyses were conducted using the R software  with tidt-tests were conducted for fractional anisotropy at each of the 50 equidistant points between younger and older adults. P values were false-discovery rate adjusted and surviving points of significant FA differences between age groups are plotted in To clarify the significant interactions of age and the 3-level ROI factors, we conducted two follow-up ANOVAs with the ROI factor reduced to 2 levels to separately contrast the control frontopontine tract with each of the other two ROIs  = 27.18, p < .001, F = 26.07, p < .001, F = 6.49, p = .003, F = 5.50, p = .008, Complete ANOVA tables for fractional anisotropy across datasets are displayed in ectively . We alsoF(299) = 26.16, p < .001, F(299) = 9.72, p = .002, F(216) = 24.34, p < .001, F(216) = 5.23, p = .023, Separate ANOVAs with the “ROI” factor either contrasting the frontopontine tract and noradrenergic bundle fractional anisotropy or contrasting the frontopontine tract and LC fractional anisotropy both yielded significant Age X ROI effects  = 0.24, p < .001 and r(243) = 0.19, p < .001, respectively. However, only three percent of the variance was accounted for (R2adj = 0.03). We did not observe any significant relationship in the BASE-II young adult cohort. We were also not able to replicate these findings in the LEMON dataset.Finally, we observed significant positive correlations between LC fractional anisotropy and noradrenergic bundle fractional anisotropy within the BASE-II older adult cohort in the left and right hemispheres, 4.Unmyelinated neurons and numerous innervations to blood capillaries may expose the LC to toxins throughout aging . During Fractional anisotropy has been observed to correlate with white matter integrity, increasing until the age of about 35–40 and decreasing into late life or with disease . AdditioWhile fractional anisotropy tended to be higher in older than younger adults within the LC itself, older adults typically showed lower fractional anisotropy than younger adults along the noradrenergic bundle white-matter ascending tract, a typical age-related pattern in white matter . The lacIn the BASE-II and LEMON datasets, age differences in the noradrenergic bundle contrasted with a lack of age differences in the right and left control white-matter frontopontine tracts, suggesting that the age effects in the noradrenergic ascending tract reflect more than just a global change in white matter. Thus, together, these data indicate that diffusivity properties of the LC and its ascending noradrenergic tract are affected by aging in opposite ways. Our findings of age differences in fractional anisotropy in the LC and its ascending tracts extend a growing set of observations of age differences in the structure of the LC in aging .To date, most in vivo findings of LC structure have relied on LC-MRI sequences that show a cross-sectional increase in LC-neuromelanin sensitive contrast from young adulthood until around age 57, at which point it levels off or declines , potentiIn the BASE-II dataset, there were no significant correlations between LC-MRI contrast from those scans and diffusivity measures from the LC or noradrenergic bundle. This suggests that the diffusivity differences reflect different structural changes than the LC-sensitive scans. An important future research objective should be to examine the relationship between LC diffusivity measures and cognition, or markers of brain health, as has been done for LC-MRI contrast . One iniOur results raise the question of what properties of the LC are changing to lead its tissue to show higher fractional anisotropy with age. One possibility could be an increase in inflammation that restricts fluid flow, as animal research has demonstrated that increases in microglial density affect diffusivity, as measured using an orientation dispersion index . AnotherMean and radial diffusivity in the LC also showed some age differences . However, given the opposite findings in the ascending white matter tract, we were still able to extract meaningful signal.Crossing fibers may indicate opposite or unexpected relationships with diffusivity values that may be related to our unexpected findings . Despite5.in-vivo in the locus coeruleus versus noradrenergic bundle (In this study, we identified unique associations of LC diffusivity in the context of healthy adults across two different data sets. We consistently observed lower fractional anisotropy in the locus coeruleus of younger adults, compared to older adults but higher fractional anisotropy in the ascending noradrenergic bundle of younger adults, compared to older adults. Fractional anisotropy is a measurement of structural integrity, and these age findings add to a growing literature highlighting age-related differences involving the locus coeruleus. To our knowledge, this is the first study to compare diffusivity differences c bundle .1


What are the characteristics of meta-analyses published in oncology journals, and what are the study characteristics associated with favorable outcomes?This scoping review of 93 meta-analyses found that many meta-analyses report on a drug, include randomized studies, and often have an author funded by industry. Methodologic quality was no different between studies with study or author industry funding and those with independent research, but industry funding was associated with the tone of the authors’ conclusion.The multiple factors associated with having a positive study conclusion suggest that future research should be performed to elucidate reasons for more favorable conclusions among studies with study or author industry funding. This scoping review examines meta-analyses published in oncology journals to identify the factors associated with having a positive study conclusion. Many meta-analyses have been conducted on a wide array of topics, and many of these have focused on treatment efficacy of drugs or bias in interventional studies on a specific topic.To examine the factors associated with having a positive study conclusion in meta-analyses in the field of oncology.All meta-analyses published between January 1, 2018, and December 31, 2021, on 5 oncology journal websites were identified and study characteristics, study results, and information on study authors were abstracted. The meta-analysis authors’ conclusions were coded as positive, negative, or equivocal, and each article subject matter was coded as one that could affect profits and marketing of a company. Whether an association existed between study characteristics and authors’ conclusions was also examined.Database searches resulted in 3947 potential articles, of which 93 meta-analyses were included in this study. Of the 21 studies with author funding from industry, 17 studies (81.0%) reported favorable conclusions. Of the 9 studies that received industry funding, 7 (77.8%) reported favorable conclusions, and of the 63 studies that did not have author or study funding from industry, 30 (47.6%) reported favorable conclusions. Studies that were funded through nonindustry sources and authors who had no relevant conflict of interest had the lowest percentage of positive conclusions and the highest percentage of negative and equivocal conclusions compared with studies with other sources of potential conflict of interest.In this cross-sectional study of meta-analyses published in oncology journals, multiple factors were associated with having a positive study conclusion, which suggests that future research should be performed to elucidate reasons for more favorable conclusions among studies with study or author industry funding. When meta-analyses are conducted, authors should be aware of potential biases. For example, bias can occur when the meta-analysis is authored by people who are experts in the topic area and who have authored studies that could be included in the review, thus resulting in a situation that promotes their work or because of findings that are in alignment with specialty-specific recommendations.1 Many meta-analyses have been conducted on a wide array of topics. In the medical specialty of oncology, many of these meta-analyses have focused on treatment efficacy of drugs or bias in interventional studies on a specific topic.10 To our knowledge, there have been no scoping analyses evaluating the general characteristics of meta-analyses in oncology journals and factors associated with bias. Therefore, we performed a scoping review to examine the factors associated with having a positive study conclusion in meta-analyses in the field of oncology.Preregistration of a meta-analysis protocol may help reduce bias occurring from post hoc decision-making and provide greater transparency in study methods, and the use of multiple databases can help to identify all available evidence rather than a possibly biased selection.STROBE) reporting guidelines.11We sought to systematically assemble a list of oncology meta-analyses by searching common oncology journal websites for all meta-analyses published between January 1, 2018, and December 31, 2021. In accordance with 45 CFR §46.102(f), this scoping review was not submitted for University of California, San Francisco Institutional Review Board approval because it involved publicly available data and did not involve individual patient data. We followed the Strengthening the Reporting of Observational Studies in Epidemiology  among oncology journals that publish meta-analyses. We searched each journal’s website by using the term meta-analysis in the search bar, allowing search results from all oncology journals affiliated with the parent journal, and limiting to the predefined dates. When there was the option, we also limited the search to research and review articles. For this review, we did not include studies that were systematic reviews only. Because we were assessing the general landscape of meta-analyses in oncology, we included all studies that had a meta-analysis design, as described by the author of the meta-analysis, and we had no further inclusion or exclusion criteria.We selected I2 for the primary outcome (or first primary outcome mentioned), heterogeneity categorization, whether the meta-analysis had been preregistered, whether an assessment of study quality or bias was performed, tool used for study quality or bias assessment, whether the data were dual reviewed, source of study funding, author conflict of interest, country of first author, and names of first and last authors. Outcome type was grouped into overall survival, tumor response , safety and adverse events, behavior, fertility, pain, risk, testing, or other. For years of included trials, if the meta-analysis included studies from the inception of PubMed or MEDLINE, we determined that this was from 1966. For heterogeneity categorization, we defined an I2 of less than 25% as low, 25% to 49% as low to moderate, 50% to 74% as moderate to high, and 75% or above as high. For each first and last author, we searched PubMed for the number of meta-analyses on which the individual was a coauthor. If we were unable to find the specific author, we searched Google Scholar for data on the number of coauthored meta-analyses. We only used Google Scholar data if there was a user profile with a verified affiliation. We then categorized the number of author publications into less than 10, 10 to 24, and greater than 24. However, for logistic regression, this was used as a continuous variable.For all studies, we abstracted data on journal, year of publication, intervention type, tumor type, outcome type, data source, years of included trials, number of studies, total number of participants, study design included, main outcome, whether the pooled results were calculated with random or fixed effects or both, rationale explained for type of modeling used (random vs fixed), We coded the overall tone of the authors’ conclusion, based on the abstract and at the end of the discussion, as positive, negative, or equivocal. We coded a study as positive if the authors’ conclusion promoted the intervention or exposure being studied. We coded a study as negative if the authors’ conclusion was negative toward an intervention or exposure . We coded a study as equivocal if we were unable to determine whether the authors’ conclusion supported or refuted an intervention or exposure. Examples of how studies were coded are provided in eTable 1 in We evaluated potential conflicts of interest in 2 ways. First, we categorized funding generally—whether the study or author received any funding from industry or not. These variables were coded as industry, nonindustry, none, or not reported. Second, we coded each article subject matter as one that could affect profits and marketing of a company  or not . We then coded each study as having potential conflicts of interest for the author , study , or independent . If a study had industry funding, we assumed that at least 1 of the authors had industry funding to conduct the study. If there was no study funding, we then looked to see if the authors reported receipt of funding from industry. Information on conflicts of interest was only obtained from information reported in the published meta-analysis.We calculated numbers (percentages) or medians (IQRs) for characteristics overall and stratified results by type of meta-analysis. We used a Fisher exact test to examine the difference in the type of potential conflicts of interest and the number of studies with positive, negative, or equivocal conclusions. We used logistic regression to examine factors associated with a positive conclusion or not. For the regression model, we included variables on study  and author characteristics  and manually removed variables one at a time if their removal resulted in a lower Akaike information criterion value. We used a 2-sided α = .05 for statistical significance. The analyses were performed in R statistical software, version 4.2.1 .Journal of Clinical Oncology (31 [33.3%]) or JAMA Oncology (28 [30.1%]). The year with the greatest number of meta-analyses published was 2020 (28 [30.1%]). The median number of years included in a meta-analysis was 51 . The median number of included studies was 23 , and the median number of participants was 7584 .Our searches resulted in 3947 potential articles , of whicThe most common tumor type was multiple (36 [38.7%]), but the most common single tumor type was breast (12 [12.9%]). The most common intervention type was drug (35 [37.6%]), and the most common outcome type was overall survival, with or without another outcome (36 [38.7%]). The most common type of study design was randomized clinical trials only (39 [41.9%]), and the most common analysis type was a random-effects model (52 [55.9%]). The number of studies that provided justification for using a random- or fixed-effects model was 35 (37.6%). Most studies were study-level (aggregate) meta-analyses (65 [69.9%]).I2 >75%) in 27 studies (29.0%), moderate to high in 7 (7.5%), low to moderate in 7 (7.5%), low in 15 (16.1%), and not indicated in 31 (33.3%). Only 35 studies (37.6%) were preregistered before being conducting, and 45 (48.4%) evaluated the quality of included studies. Sixty-one studies (65.6%) had dual review of the abstracted data.Heterogeneity was high (The most common funding source was a nonindustry source (38 [40.9%]), but many did not disclose funding (33 [35.5%]). Sixty-three studies (67.7%) had authors who reported industry conflict of interest. The US was the most commonly represented geographic area (22 [23.7%]), but 54 studies (58.1%) had authors from multiple countries. Sixty-seven (72.0%) of the first authors and 51 (54.8%) of the last authors had published fewer than 10 previous meta-analyses, but it was also common for last authors to publish 25 or more meta-analyses (17 [18.3%]). The κ statistic for study conclusion was 0.59, indicating moderate agreement. Of the 21 studies with author funding from industry, 17 studies (81.0%) reported favorable conclusions. Of the 9 studies that received industry funding, 7 (77.8%) reported favorable conclusions, and of the 63 studies that did not have author or study funding from industry, 30 (47.6%) reported favorable conclusions . StudiesIn the adjusted logistic analyses , having 12 exploring the role of financial conflict of interest on the conclusions of cost-effectiveness research.In this scoping review, we found that for meta-analyses in top oncology journals, drug interventions were the most studied intervention type, but biomarkers and patient characteristics were also commonly studied intervention types. This study also found that intervention-related industry payments to authors or study sponsorship were associated with a higher probability of a meta-analysis finding favorable results for an intervention, when compared with non–industry-funded studies. These reports are consistent with results from a prior study13 Although it is often accepted that registration trials are funded by and written by employees of industry, meta-research, including meta-analyses and cost-effectiveness studies that inform health policy, may be more susceptible to industry influence (either by choice of topic or by methodologic choices within topics). Moreover, a prior Cochrane review14 found that conflicts of interest are common and associated with favorable conclusions for general medicine drug and device studies. A previous report12 found that meta-analyses on cost-effectiveness studies in oncology resulted in a 40-fold greater likelihood of concluding that a drug is cost-effective. However, potential conflicts of interest are often not reported in general or oncology-specific meta-analyses.15Potential conflicts of interest are well recognized for being a factor for biasing study results.16 rather than important clinical end points, which may be more likely to be positive. Another explanation is that in contrast with randomized clinical trials with prespecified statistical plans, cost-effectiveness research and meta-analyses have more analytic flexibility. Indeed, analytic choices can lead to widely divergent outcome estimates.17Although there are several possible explanations for why industry-funded studies are more often positive, one may be that industry is incentivized to focus on avenues of research that they know will be successful. Conversely, industry-funded studies may also rely more on surrogate end points19We found that many meta-analyses were traditional meta-analysis, with a study as the unit of observation, but one-third are other types. Network meta-analyses are becoming popular because they allow researchers to compare effectiveness of multiple interventions. Still, approximately 20% of meta-analyses used patient-level data. Besides the obvious deciding factor of whether the researcher has access to individual-level data, the advantages and disadvantages of each type of meta-analysis has led to debate about which one is better, although patient-level meta-analysis may be more preferred when determining efficacy.20 However, if there was no heterogeneity in study results, there would be little reason to conduct a meta-analysis because the answer would be known. An advantage of a patient-level meta-analysis is the ability to investigate heterogeneity due to individual-level effect modifiers and not only study-level factors or ecologic summaries of individual-level factors . To balance the expectation of equipoise and conducting appropriate tests that accommodate heterogeneity in study results, researchers have suggested alternative methods of statistical analysis21 and ways in which to present the data, such as using prediction intervals in addition to the traditional I2 value.22 Although we did not perform an exhaustive review of reporting quality, we found that, for the items we evaluated, the quality of reporting in studies was moderate at best, with most studies (65.6%) having dual review of study data, but fewer studies reporting the quality of included studies (48.4%), providing a rationale for type of model used (37.6%%), or preregistering their study (37.6%).We found that most meta-analyses had high heterogeneity in their pooled analysis, yet, although most studies used a random-effects model, there was no justification provided for using this method of analysis. Some authors have suggested that even with the use of a random-effects model, if there is a high degree of heterogeneity, the results should not be pooled.Our analysis was limited by several factors. First, we included a search of only 5 oncology journal websites over the course of 4 years, and our results may not be applicable to the meta-analysis literature at large. These journals may have a better editorial process than other journals often selecting higher quality manuscripts and the results are likely a better-case scenario because of better study methods. Second, our analysis was purely descriptive, and we did not examine factors associated with our findings. Third, we made several assumptions with the years of included studies , so our years of study inclusion may be overestimated. Fourth, we were not able to determine numbers of meta-analyses that some authors had published, so data are limited for those variables.In this scoping review of meta-analyses in oncology journals, we found that most studies either did not report on heterogeneity or had a high degree of heterogeneity, and most studies used a random-effects model but did not provide justification for its use. We also found that study funding or author conflicts of interest and having a first author with more published meta-analyses were associated with more favorable conclusions. The multiple factors associated with having a positive study conclusion suggest that future research should be performed to elucidate reasons for more favorable conclusions among studies with study or author industry funding.


Increasing NAD+ is becoming a therapy for metabolic dysfunction; however, the impact of daily NAD+ fluctuations remains unknown. Here, we demonstrate that time-of-day determines the efficacy of NAD+ treatment for diet-induced metabolic disease in mice. Increasing NAD+ prior to the active phase in obese male mice ameliorated metabolic markers including body weight, glucose and insulin tolerance, hepatic inflammation and nutrient sensing pathways. However, raising NAD+ immediately before the rest phase selectively compromised these responses. Remarkably, timed NAD+ adjusted circadian oscillations of the liver clock until completely inverting its oscillatory phase when increased just before the rest period, resulting in misaligned molecular and behavioral rhythms in male and female mice. Our findings unveil the time-of-day dependence of NAD+-based therapies and support a chronobiology-based approach.The circadian clock is an endogenous time-tracking system that anticipates daily environmental changes. Misalignment of the clock can cause obesity, which is accompanied by reduced levels of the clock-controlled, rhythmic metabolite NAD The timing of NAD + supply determines its efficacy to treat metabolic disease. Here, the authors show that increasing NAD + at the early active phase maximizes weight loss and glucose regulation in mice. NAD + can displace the phase of the liver clock which can cause circadian misalignment. The main cause appears as combined inappropriate nutrition and sedentary lifestyles. Overweight, insulin resistance, β-cell dysfunction, increased circulating glucose and lipids and non-alcoholic fatty liver disease (NAFLD) characterize the pathophysiology of T2D2. Countless research efforts have explored pharmacological treatments for T2D and associated pathologies leading to promising compounds, which together with lifestyle interventions constitute first-line treatments3. During the last few years, the circadian system has been increasingly recognized as a key actor for development and treatment of diet-induced metabolic dysfunction. Yet, circadian rhythms in the clinical practice remain largely overlooked and time-of-day is hardly considered in treatment decisions8.In the last few decades, the prevalence of obesity has become epidemic through the world and is a major risk factor for type 2 diabetes (T2D)9. Aligned synchrony between all body clocks maintains homeostasis and health, for example, by adjusting metabolic performance to daily environmental fluctuations. Conversely, persistent circadian misalignment is a cause of severe diseases, including obesity and metabolic syndrome, T2D, or cardiovascular disease, amongst others12. At the molecular level, the circadian machinery is expressed in almost all cell types and consists of transcriptional-translational autoregulatory feedback loops. The positive loop is driven by the CLOCK:BMAL1 transcriptional activator, which rhythmically binds to E-box genomic elements, thereby activating transcription of many genes including the circadian repressors, Period (Per1-3) and Cryptochrome (Cry1-2). PER:CRY complexes directly repress CLOCK:BMAL1 leading to transcriptional silencing. A number of interlocked regulatory loops, such as the one governed by RORs/REV-ERBα to regulate Bmal1 expression, intertwine to confer complexity, redundancy, and robustness to circadian rhythms13. Consequently, a set of clock-controlled genes (CCGs) ranging from 5–25% depending on the tissue or cell type, display transcriptional circadian rhythms14. Notably, rhythmic transcripts are functionally related, including rate-limiting enzymes, hence providing means to adjust the pace of many metabolic pathways around the day and driving rhythms in the tissue metabolome18. A paradigmatic example is illustrated by daily rhythms in nicotinamide adenine dinucleotide (NAD+) bioavailability, imposed by circadian oscillations in the clock-controlled gene Nampt, the rate-limiting enzyme for the NAM salvage pathway to NAD+20. Several lines of evidence demonstrate that the molecular clock and NAD+ oscillations sustain mitochondrial function and bioenergetics, manifested in daily rhythms in respiration, fatty acid oxidation, or nutrient utilization25. Indeed, it is considered that clock-controlled NAD+ biosynthesis occupies a fundamental position connecting circadian metabolic pathways28.Circadian rhythms are evolutionary conserved 24-h cycles in physiology dictated by an intrinsic circadian clock. In mammals, the suprachiasmatic nucleus (SCN), a master timekeeper in the hypothalamus, receives photic cues from the retina to align internal and external time. The SCN distally synchronizes ancillary oscillators in peripheral tissues. Importantly, certain cues such as nutritional inputs effectively synchronize peripheral clocks+ and its phosphorylated and reduced forms NADH, NADP+, and NADPH, are coenzymes for hydride transfer enzymes, crucial to biological redox reactions. NAD+/NADH ratio is a basic determinant of the rate of catabolism and energy production30. In fed state or nutrient overload NAD+/NADH ratio falls, and a prolonged redox imbalance potentially leads to metabolic pathologies, such as diabetes31. Along these lines, extensive research demonstrates that NAD+ levels significantly decline in metabolic tissues of mice and patients with obesity37. NAD+ decay itself may contribute to metabolic dysfunction by distinct mechanisms, including increased oxidative stress and ROS production, disbalance in the oxidative-reductive capacity, disrupted Ca2+ homeostasis, or reduced activity of sirtuins39; a class of deacetylase enzymes using NAD+ as cofactor and known to influence mitochondrial function and metabolism. In recent years, NAD+ has emerged as a target for the treatment of metabolic diseases, as boosting endogenous NAD+ levels has been proven effective against diet-induced metabolic pathologies, including insulin resistance, hyperglycemia, hypertriglyceridemia and NAFLD45. All these studies aim to increase NAD+ levels either genetically or pharmacologically, yet they mostly overlook the circadian trait of NAD+ bioavailability. Consequently, the implications of circadian rhythms in the function and effectiveness of NAD+ boosters as a therapy for diet-induced metabolic dysfunction remain largely obscure.NAD+ levels. To approach this question, we used a mouse model of diet-induced obesity (DIO), which is known to present decreased, non-rhythmic levels of NAD+17, and pharmacologically recovered daily rhythms of NAD+ with a peak at the onset of the active phase. To do so, we used a daily timed intraperitoneal (IP) injection with NAD+ itself at ZT11. We show that obese mice with enforced NAD+ oscillations improved metabolic health, significantly lost weight, and corrected NAFLD. Our analyses revealed that hepatic transcriptional signatures of inflammation disappeared in these mice. Indeed, hepatic signaling involving AMPK, AKT, mTOR was rewired after restoring rhythmic NAD+ in obese mice, providing increased insulin sensitivity during the active period. Together, we demonstrated that a single daily injection with NAD+ treats the pathophysiology of diet-induced obesity, with comparable efficiency to NAD+ precursors. Remarkably, these metabolic and molecular improvements were not recapitulated by obese mice with antiphase increase of NAD+, at the onset of the rest phase, which showed only selective recovery of metabolic health. Further analyses demonstrated that lipid oxidative pathways and the molecular clock are central mediators for phase-dependent, differential effects of NAD+. Particularly, NAD+ provided at the onset of the rest phase uncoupled oscillations between central and peripheral clocks, by means of inverting the phase of the hepatic clock while food intake and activity remained rhythmic. Collectively, our findings reveal that timed NAD+ supply can shape the oscillatory phase of the hepatic molecular clock in vivo and expose a previously unappreciated time-dependent effect of NAD+ as a treatment for metabolic dysfunction, paving the way for chronotherapy and personalized medicine.In this work, we aimed to characterize the metabolic consequences of rhythms in NAD+ administration improves metabolic fitness in obesity, we used a mouse model of diet-induced obesity (DIO) where instead of increasing NAD+ by chronic supplementation with metabolic precursors, we directly supplied the metabolite itself in a daily single IP injection scheduled at ZT11, corresponding to an hour before the normal circadian rise of hepatic NAD+46. Hence, after 8 weeks of high-fat diet (HFD) feeding, mice were treated for 22 days with saline solution (HF group) or 50 mg/Kg of NAD+  with respect to their obese non-treated littermates (HF), which was sustained after 22 days . At the end of the treatment, hepatic NAD+ content was measured by HPLC, showing the expected oscillation with a peak at ~ZT12 in control mice .At week 8 on HFD, mice displayed expected increase in body weight which was accompanied by significantly higher caloric intake during both light and dark periods47 Fig. . NotablyCD, Fig.  which isCD, Fig. 16,21,36,P < 0.001 Two-way ANOVA, Tukey post-test) and a six-hour phase delay in the oscillatory pattern . After 20 days of treatment, this improvement was also apparent at ZT4 . As both insulin and glucose levels were lower in NAD+-treated mice, insulin sensitization might occur. Accordingly, glucose clearance upon insulin IP injection was largely enhanced by NAD+ chronotherapy . Interestingly, NAD+ treatment at ZT11 promoted a slight, albeit not significant, improvement in insulin tolerance with respect to control lean mice when tested at ZT16  was used to semi-quantitatively assess hepatic steatosis Fig. , revealie16 Fig. . Additioels Fig.  and augmels Fig. . Togethe50 was overexpressed across the day in the livers from HFD-fed mice, while those treated with NAD+ showed markedly reduced PPARγ levels  and dark (ZT18) phases in mouse livers from CD, HF, and HFN groups. 76 common genes were differentially expressed (DE) between day and night in all groups , Cry1, Nr1d2 (Rev-Erbβ), Rorc, Tef, Nfil3 or Ciart . Out of these, 322 transcripts were exclusively fluctuating in the CD group, 1327 fluctuated solely in the HF, interestingly, 306 newly fluctuating transcripts appeared in the HFN group  or night (ZT18). At ZT6, 724 hepatic transcripts were significantly DE between CD and HF mice, while 936 transcripts varied when comparing HF and HFN groups, with 182 (12%) overlapping transcripts  mice to control conditions  showed that IL6-JAK-STAT3 and TGFβ signaling were the highest enriched hallmarks  . As shown, these hepatic expression changes at ZT18 were accompanied by improvement of hyperlipidemia and fatty liver traits after restoring NAD+ oscillations in obese mice  are previously described direct targets of FOXA257  Fig. , with siice Fig. . Pathwayrks Fig. ; and de rks Fig. . Accordi+ oscillations in obese mice. To confirm this at the molecular level, we first evaluated phosphorylation of AKT1, a key kinase effector of insulin signaling59, along the day. As previously described, AKT1 phosphorylation at Ser 474 (p-AKT(S473)) appeared cyclic in CD fed mice60, with a peak at ZT18  at ZT12 , hence imposing daily oscillations to insulin signaling. Furthermore, diurnal rhythms in AMPK phosphorylation at T172 were also restored by NAD+ treatment in obese mice, although with a unique peak at ZT12, which was six hours phase delayed compared to their control, lean littermates  and HNF4α TFs, both implicated in maintaining lipid homeostasis in the liver77. Also, a motif recognized by IRF3 and NR4A1 (Nur77) appeared significantly enriched (P = 1e-5), and interestingly, Nur77 has been shown to regulate the cytoplasmic shuttling of LKB1, hereby phosphorylating and activating AMPK78. Together, these data indicate that oscillatory NAD+ in obese mice activates a gene expression program favoring processes highly demandant for vesicle trafficking, such as translocation of membrane receptors or autophagy, and reinforce the idea of pharmacological supply of NAD+ preferably targeting activation of AMPK even in the context of high caloric feeding.A search for de novo motif enrichment within the promoters of NAD+ oscillations depend on the time of the day, we supplied NAD+ in opposite phase to its natural rhythmicity, hereby at the end of the active phase in mice, ZT23 (HFN23 group). In these HFN23 mice, oscillations of hepatic NAD+ were induced with antiphase respect to CD and HFN mice, showing a peak at ZT0 and decreasing at ZT12-18 , glucose and insulin tolerance showed non-significant improvement compared to the HF-fed mice. Actually, the NAD+ treatment at ZT11 was significantly more favorable to improving glucose homeostasis than at ZT23 , NAD+ supply at ZT11 showed still a significantly better performance than at ZT23 . Opposite, hepatic steatosis was reduced to a similar extent in HFN and HFN23 groups , the macrophage recruiter gene Csf2,  and macrophage markers   Fig. , evidencBPα Fig. , which t+ at ZT23 to obese mice did not recapitulate hepatic AKT phosphorylation and activity, as did at ZT11 , but not at ZT23 (HFN23). Indeed, fatty acid oxidation-related genes were highly expressed at the end of the rest period (~ZT12)86; yet, unique to the HFN group was that the breadth of transcriptional activity further extended through the active period, reaching significantly higher levels than in the non-treated, obese mice (HF) at ZT18 . Hereby, expression of these genes at ZT18 was altered depending on the time of NAD+ treatment, in a way that the treatment at ZT11 significantly enhanced their expression, whereas in mice treated at ZT23, expression was significantly reduced to levels largely comparable to the CD littermates  at Ser79 and ACC2 at Ser212. These in turn downregulate the production of malonyl-CoA, the major substrate for fatty acid synthase (FAS) and a strong inhibitor of carnitine palmitoyl transferase 1 (CPT-1). Consequently, fatty acid synthesis is suppressed in favor of lipid oxidation, partially through activation of the rate-limiting step sustained by CPT-1T18 Fig. . Hence, ups Fig. . As expe+ at ZT23 presented some metabolic ameliorations mostly consisting of improved basal circulating glucose levels and reduced hepatic steatosis and inflammatory markers, we did not find consistent changes in gene expression or nutrient sensing signaling. Intriguingly, our microarray data showed that transcripts with highest fold-change after NAD+ treatment were Metallothionein 1 and 2 (Mt1 and Mt2), two antioxidants and longevity regulators known to protect from HFD-induced obesity89, and these transcripts were significantly more overexpressed in obese mice treated with NAD+ at ZT23 . A similar case was found for the gene lipocalin 2 (Lcn2), which encodes for a secreted protein protective against NAFLD90. Hence, while NAD+ chronotherapy works optimally at ZT11, its supply at ZT23 induces distinct protective pathways responsible for a mild, albeit noticeable, improvement of HFD-induced metabolic disease.While obese mice treated with NAD+ at ZT11 and ZT23 led to significantly different consequences in metabolic fitness and daily gene expression in the liver of obese mice. Hence, we reasoned that the molecular clock might be responsible for daily variations in the effectiveness of the treatment. Thereby, we compared the hepatic clock protein expression along the day between obese mice treated at ZT11 and at ZT23  and repressors  . To determine whether the observed antiphase dynamic of the clock transcriptional regulators was functional, we selected the genes Dbp, Tef, Nfil3, and Noct, whose expression is directly and mostly controlled by the core clock machinery, and analyzed their hepatic expression around the day  or at ZT17 (HFN17). We found expected phase advance in clock protein oscillations in livers from HFN5 mice, while treatment at ZT17 resulted in phase delayed oscillations  Fig. . Indeed,day Fig. . Coincidion Fig. . To furt95, and the NAD+ precursor NR increases BMAL1 recruitment to chromatin in livers from aged mice46, we hypothesized that inverted expression of clock genes in HFN23 might be driven by time-specific recruitment of BMAL1 to chromatin. To test this, we performed ChIP analyses to measure BMAL1 binding to regulatory E-boxes of clock and clock-controlled genes . A non-related region at the 3’ UTR region of Dbp gene was used as a negative control. We further evaluated the effect of NAD+ supplementation in the expression of NAD+ biosynthesis and salvage genes Nmrk1, Nampt, Nmnat3, and Nadk which also showed inverted phase specifically in HFN23 mice  or 30 min (HFN_23) windows , which was previously described to boost hepatic NAD+ after IP injection in one hour101. Hence, three weeks with NAM chronotherapy performed best when applied at ZT11 to improve body weight, GTT and ITT , nicotinic acid (NA) or nicotinamide mononucleotide (β-NMN), while some tissues such a liver produce NAD+ de novo from tryptophan, in a much less efficient biosynthetic pathway104. At this regard, the NAD+ precursors NAM, NMN and NR have been preferentially used as NAD+ boosters; however, we set up a therapy with NAD+ because the limited data tracing metabolic fluxes suggest distinct, tissue-specific effects of NR and NMN105. Moreover, NAD+ uptake appears fast and effective in cells, and a mitochondrial active transporter has been recently described109. Yet, to gain insights into the bioavailability of NAD+ precursors in our study, it would be necessary to unravel the hepatic NAD+ metabolome in all tested conditions, as for example, the possibility that time-dependent decline in NADPH and NADP+ levels in livers from obese mice110 contributes to differences between HFN and HFN23 mice cannot be ruled out, constituting a limitation of our study. However, we demonstrated that hepatic NAD+ levels raised within an hour after IP injection in obese mice, and followed a circadian turnover when administered at ZT11, at the onset of the active phase 135. Indeed, both NAD+ consumers SIRT1137 and SIRT321 provide reciprocal regulation to the clock machinery to modulate circadian transcription and metabolism in the liver. Furthermore, recent research shows that a NAD+-SIRT1 interplay mediates deacetylation and nuclear translocation of PER2 and, in line with our results, shapes BMAL1 function, while this control is altered in livers from aged mice46. Through activation of SIRT1 and SIRT3, it is also possible that rising NAD+ at ~ZT12 might contribute to rhythmic lipid oxidation and mitochondrial function driven by protein acetylation, including PPARγ138, while keeping the hepatic clock aligned to the external time. Yet, the regulation of the circadian system by sirtuins in health and disease remains to be fully disentangled. Circadian misalignment imposed by antiphase NAD+ in our HFN23 and HFNAM_23 mice might obstruct metabolic improvements, through uncoupling of the central light-synchronized and peripheral NAD+-synchronized clocks. Although hepatic neutral lipid content was reduced independently of time-of-treatment . Food intake and body weight were measured once a week. For daily food intake measurements, mice were single housed, and measurements were recorded for 1 week. Female mice were used for the experiments using NAM as a NAD+ precursor, and the rest of the experiments were performed in male mice.Four-week-old C57Bl/6 J mice were obtained from the Biological Models Unit at the Instituto de Investigaciones Biomédicas . The mice were kept under a 12:12-h light:dark cycles. Food and water were provided ad libitum. Temperature and humidity were constantly monitored. Mice were randomly distributed to four groups. The control group was fed during 11 weeks with normal chow , bearing 24% calories from protein, 18% calories from fat, and 58% calories from carbohydrates. The other three experimental groups were fed a high-fat diet , consisting of 15% calories from protein, 53% calories from fat and 38% calories from carbohydrates, and customized to match NADAll animal experimental procedures were reviewed and approved by the Internal Committee for the Care and Use of Laboratory Animals (CICUAL) at the Instituto de Investigaciones Biomédicas, , and are registered under protocol no. ID240.+ and NAM were purchased from SIGMA  and were dissolved in 0.9% NaCl isotonic saline solution and filter sterilized. To determine the NAD+ dose, we wanted to keep two premises: (1) to keep NAD+ levels into the physiological range, and (2) avoid undesirable secondary effects of high doses. To do so, we chose the range of tested doses based on previous reports151, and treated mice with IP injection of 800, 100, 50, or 10 mg/kg body weight, while keeping a constant volume of approximately 180 μl. Control mice were injected with isotonic saline solution. C57Bl/6 J male mice (n = 3) were IP injected, and sacrificed one hour later. NAD+ was measured by HPLC as described below. Because we planned on a chronic treatment, the minimum dose inducing a statistically significant increase in hepatic NAD+ with respect to control livers was selected as the experimental dose . Hence, for all experiments, mice were IP injected with 50 mg/kg of NAD+ for 20 consecutive days, either at ZT11 (one hour before lights off), or at ZT23 (one hour before lights on). Of note, we didn´t find differences in hepatic NAD+ at a dose of 10 mg/kg, a reason why we did not try lower concentrations. The dose for NAM treatment in female mice (200 mg/kg) was selected based on previous reports154.NAD+ measurements were performed according to Yoshino and Imai 2013155, with subtle modifications. 100 mg of frozen tissue were processed in a final volume of 2 ml of 10% HClO4 with a Polytron homogenizer (Kinematica CH-6010 Kiriens-Lu) and centrifuged at 12,000 × g for 5 min at 4 °C. The supernatant was neutralized adding a one-third volume of 3 M K2CO3, and vortexed. After 10 min of incubation on ice, samples were cleared by a 12,000 g centrifugation at 4 °C during 5 min. The supernatant was diluted at 30% with 50 mM phosphate buffer . 50 μl of the samples were analyzed using a 1260 infinity quaternary LC VL HPLC system (Agilent) attached to a diode array detector. Analytes were separated on a ZORBAX Eclipse XDB-C18 4.6 × 150 mm, 5 μm column (Agilent p/n 993967-902). For the HPLC, the gradient mobile phase was delivered at a flow rate of 1 ml/min, and consisted of two solvents: (A) 50 mM phosphate buffer pH 6.8 and (B) methanol 100%. The initial concentration of A was 100%, the solution was held into the column for 5 min and then B was progressively increased to 5% over 1 min, held at 5% B for 5 min, followed by an increase to 15% B over 2 min, held at 15% B for 10 min and returned to starting conditions of 100% A in 1 min, and held at 100% A for 6 min. NAD+ was detected using a sample wavelength of 261 nm and reference wavelength of 360 nm. Adequate standards including NAD+ were used for calibration and identification of the retention/migration time of NAD+ within the samples. Instrument control, data acquisition, and analysis were performed using the Agilent ChemStation system for LC, according to manufacturer’s instructions. NAD+ levels were quantitated based on the peak area in the chromatograms compared to a standard curve and normalized to tissue weight.NADAt 8, 10, and 11 weeks of experimental paradigms, mice were subjected to either 12 h or 5 h of fasting, followed by a glucose tolerance test (GTT) or an insulin tolerance test (ITT) respectively. For the GTT, IP injection of D-glucose (SIGMA cat no. G7021) at 2 mg/kg was used, while for ITT, human insulin (Eli Lilly cat. HI0210) at 0.6 U/kg was IP injected. Circulating glucose was measured from a tail-tip blood drop, using an ACCU CHEK active glucometer (ROCHE) at time points 0 (before injection) and 15, 30, 60, and 120 min after IP injection of either glucose (GTT) or insulin (ITT). Experiments were performed per triplicate, using 5–6 mice per experiment.Blood serum was collected postmortem by cardiac puncture. Triglycerides (TG) in serum and liver were measured using the Triglyceride Colorimetric Assay Kit . Free fatty acid content was determined with the Free Fatty Acid Fluorometric Assay Kit . Serum insulin and leptin levels were measured by ELISA, using the Ultra-Sensitive Mouse Insulin ELISA Kit  and the Mouse Leptin ELISA Kit  according to the manufacturer’s instructions. Hepatic cholesterol was determined using a Cholesterol Quantitation Kit . Absorbance/fluorescence was measured using a Synergy H1 microplate reader (BioTek).n = 10 mice, and three technical replicates) was registered using a portable digital thermometer (BIOSEB) every 3 h throughout 24 h. For the acquisition of infrared thermography, mice were placed inside an acrylic box in darkness. Thermal images were acquired at ZT12 using an Inframetrics C2 Thermal Imaging System Compact Pocket-Size camera (FLIR Systems) with a frequency of 9 Hz, thermal sensitivity <0.10 °C, resolution 80 × 60 (4800 pixels) and temperature range of 14 to 302 ° F. . Images processing was performed using FLIR-Tools software v 5.13.17214 (2015 FLIR® Systems).Rectal temperature in mice (156. Oil Red O working solution (3.75 mg/ml) was applied on OCT-embedded liver sections for 5 min at RT. Slides were washed twice during 10 min. in water, and mounted in vectashield mounting media . The images were captured with the Olympus camera DP70 system using the DPController v 1.1.1.65 software, coupled to a Olympus BX51 microscope with the DPManager software v. 1.1.1.71, using a ×40 magnification. The background was corrected by white balance and was selected as a blank area outside the section. For representative images, some sections were stained with Gil I haematoxylin. Surface of lipid droplets was quantified using the ImageJ software (v 1.53), by converting RGB to 8-bit grayscale images, and then using the “analyze particles” plug-in to measure the area and size of the lipid drops157. Three frames per biopsy were used for image analyses and quantification .Frozen OCT-embedded liver tissues were cut into 10-μm sections using a Leica cryostat and air dried for 10 min at room temperature. Slides were briefly washed with PBS and fixed for 2 min with 4% fresh paraformaldehyde. Preparation of Oil Red O  working solution and staining of slides was performed according to Mehlem et al.3 region above the optic chiasm was dissected out using microscissors. Tissues were placed in microcentrifuge tubes in 100 µl of Trizol and kept at −80 °C until use. Total RNA was subsequently extracted and resuspended in 12 µl of water.For gene expression analysis from the SCN, frozen brains were placed on ice, and the 1 mmTM Reagent, Invitrogen, cat. no. 15596018). The homogenate was incubated for 5 min at RT, then 0.1 ml of chloroform was added, shaken, and incubated at RT for 3 min followed by a centrifugation during 15 min at 12,000 × g at 4 °C. The upper phase was extracted, and 0.25 ml of isopropanol was added. After a 10 min incubation at RT, RNA was precipitated by centrifugation for 10 min at 12,000 × g and 4 °C. The RNA was washed with 1 ml of 75% ethanol and resuspended in 20 µl of molecular biology grade water . 2 µl of the sample were used to quantify its concentration and assess its quality in a NanoDrop (Thermo Scientific).20 mg of liver tissue or the dissected SCN, were homogenized  for 30 seconds with 0.5 ml of Trizol . 500 ng of RNA were mixed with 2 µl of 5X iScript Reaction Mix and 0.5 µl of the enzyme iScript Reverse transcriptase in a volume of 10 µl. The thermal cycler (Axygen MaxyGeneTM II) was programmed as follows: Alignment for 5 min at 25 °C, reverse transcription for 20 min at 46 °C and inactivation for 1 min at 95 °C. The reaction was cooled to 4 °C and diluted to 5 ng/µl.It was performed using the kit iScriptB2m, Ppia, and Tbp. The geometric mean was used to determine Ct values of the housekeeping genes and expression values for the genes of interest were calculated using ΔCT methodology. Primer sequences are available in Supplementary Data The reactions were performed in a final volume of 10 µl, adding 5 µl of the Universal SYBR Green Super Mix reagent , 1 µl of 2.5 µM forward and reverse primers, and 7.5 ng of cDNA per reaction. The thermal cycler  was set to the following program: 30 s at 95 °C followed by 40 cycles of 5 s at 95 °C and 30 s at 65 °C. Single-product amplification was verified by an integrated post-run melting curve analysis. Values were normalized to the housekeeping genes (ΔCT),where ΔCT is the difference of CT values between S18 gene and mtCOX1 gene158.10 mg of liver were used to extract DNA with the DNeasy Blood & Tissue Kit , according to the manufacturer’s instructions. Quantitative PCR was performed using 7.5 ng of DNA and 2.5 μM of S18 and mtCOX1 primers as described for cDNA quantification, with a program of 20 min at 95 °C, followed by 50 to 55 cycles of 15 s at 95 °C, 20 s at 58 °C and 20 s at 72 °C. Single-product amplification was verified by an integrated post-run melting curve analysis. 5–6 mice were analyzed for each time point and condition, with two technical replicates. mtDNA content using the formula: 2 × 2n = 3 biological replicates). The Clariom™ D Array consists of 66100 genes (transcript clusters), 214900 transcripts, 498500 exons and 282500 exon-exon splice junctions from Mus musculus. Sequences are mapped to the National Center for Biotechnology Information (NCBI) UniGene database. The arrays were scanned in the GeneChip Scanner 3000 7 G (Affymetrix) and the GeneChip Command Console Software (v 4.0.3) was used to obtain the.CEL intensity files. Normalized gene expression data (.CHP files) were obtained with the Transcriptome Analysis Console (TAC v4.0.1.36) software using default parameters. Changes in gene expression  were subjected to functional analyses using the “Compute Overlaps” tool to explore overlap with the CP  and the GO:BP  gene sets at the MSigDB (molecular signature database) v7.0. The tool is available at: https://www.gsea-msigdb.org/gsea/msigdb/annotate.jsp, and estimates statistical significance by calculating the FDR q-value. This is the FDR analog of the hypergeometric P value after correction for multiple hypothesis testing according to Benjamini and Hochberg. Gene set enrichment analysis (GSEA) was performed using GSEA v. 4.0.3.54 to determine the enrichment score within the Hallmark gene set collection in MSigDB v7.0159, selecting the Signal2Noise as the metric for ranking genes. The findMotifs.pl program in the HOMER software v 2.0160 was used for motif discovery and enrichment, searching within the genomic regions encompassing 300 Kb upstream and 50 Kb downstream the TSS, and selecting 6–8 bp for motif length. Motif enrichment is calculated by findMotifs.pl using the cumulative hypergeometric distribution.Liver RNA samples for microarray analysis were prepared using our previously described procedures, with slight modifications. Briefly, total RNA was first extracted with TRIzol Reagent (Invitrogen), then cleaned with RNeasy Mini purification Kit (QIAGEN cat. no. 74106) according to the manufacturer’s RNA CleanUp protocol. RIN values (≥7.0) were validated with an Agilent Bioanalyzer 2100. 900 ng of total RNA per sample was used as a template to obtain cDNA with the GeneChip cDNA synthesis Kit . Microarray experiments were conducted by the Microarray Unit at the National Institute of Genomic Medicine  using the mouse Clariom™ D Assay (Applied Biosystems™), as per manufacturer’s instructions. Microarray experiments were performed in triplicate  for 10 min at RT. The reaction was stopped by adding a NaOH (6 M) for 10 min. The absorbance of the samples was read in a spectrophotometer  at 370 nm and the mean absorbance of control tubes (RIPA buffer) was then subtracted. To calculate the PCO concentration expressed as nmol PCO/mg protein, we used the following equation:The determination of the carbonyl content was performed from total hepatic protein extracts (0.5 mg/ml), following a previously published protocol3VO4 1 mM, NaF 0.5 mM). Total protein was quantified with Bradford reagent , and 25 μg of extract were suspended 1:6 (v/v) in 6× Laemmli buffer , separated on sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), and transferred onto PVDF membranes (Merck-Millipore), using the Mini-PROTEAN electrophoretic system (Bio-Rad). Membranes were blocked using non-fat milk in PBST buffer for one hour and incubated with the corresponding primary antibody overnight at 4 °C. Membranes were washed three times with PBST and incubated with the secondary antibody for 5 hrs at RT. Antibodies used in this study were: From Cell Signaling: PPARγ (2443), AKT (9272), Phospho-AKTSer473 (9271), AMPKα (5831), Phospho-AMPKαThr172 (50081), mTOR (2893),), Phospho-mTORSer2448 (5536), Phospho-p70 S6KThr389 (9234), Phospho-4E-BP1Thr37/46 (2855), RSK1/RSK2/RSK3 (9355), Phospho-p90RSKSer359 (8753), REV-ERBα (13418), ULK1 (8054), Phospho-ULK1Ser555 (5869), all diluted 1:1000; from Santa Cruz: C/EBPα ; from Abcam: BMAL1 ; from Alpha Diagnostics International: PER2 (PER21-A 1:2000); from Bethyl Laboratories: CRY1 (A302-614A 1:1000); from Sigma: α-Tubulin ; from Genetex: GAPDH-HRP  and P84  The secondary antibodies were Anti-rabbit IgG  or Anti-mouse IgG , conjugated to horseradish peroxidase. For detection, the Immobilon Western Chemiluminescent HRP Substrate  was used and luminescence was visualized and documented in a Gel Logic 1500 Imaging System (KODAK). Protein bands were quantified by densitometric analysis using Image Studio Lite Version 5.0 software (LI-COR biosciences). 3–5 biological replicates were used for each quantification. Uncropped, unprocessed scans of the blots are available in the Source Data file.Livers were lysed in 1× RIPA buffer supplemented with a protease/phosphatase inhibitor cocktail  was used for profiling inflammation cytokines, following the manufacturer’s instructions. Absorbance at 450 nm was measured using a Synergy H1 microplate reader (BioTek).48, with subtle modifications. Briefly, 20–60 mg of fresh liver from mice sacrificed at ~ZT16 were weighed, and samples were minced and homogenized in 300 μl homogenization buffer  at 4 °C. Then, 5 μl palmitic acid [14 C] 100 μC/ml (Perkin Elmer) was added, and these lysates were incubated for 2 hr at 37 °C. Eppendorf tubes were prepared to contain small pieces of Whatman paper in the cap of the tube, which were wet with 20 μl NaOH 3 M, while 150 μl 70% perchloric acid was placed inside the tube. Lysates were added to these tubes and incubated 1 h at 37 °C. The trapped 14CO2 was determined transferring the filter discs to a scintillation vial with 4 ml of scintillation liquid and measuring in a Beckman LS6500 scintillation counter.Fatty acid oxidation was quantitated ex vivo following our previous protocols162 with modifications as follows. Mice were sacrificed by cervical dislocation at ~ZT16, and ~100 mg of liver was dissected and placed on ice on mitochondrial isolation buffer  and centrifuged at 800 g for 10 min at 4 °C. The tissue was homogenized using the Polytron tissue homogenizer at low potency for 8 seconds and centrifuged at 800 g (10 min at 4 °C). The supernatant was collected in 15 ml falcon tubes and centrifuged at 8000 × g (10 min at 4 °C). The resulting pellet containing the mitochondria was washed three times with 1 ml of MIB1 and resuspended on 0.5 ml of mitochondrial assay solution  with the addition of substrates . Total protein was determined using the Qubit® 3.0 Fluorometer and 14 µg of isolated mitochondria were diluted in MAS1 buffer with substrates with or without the presence of the CPT-1 inhibitor, etomoxir (3 µM) and loaded per well in the XFe96 plate. The plate containing mitochondria was centrifuged at 2000 g for 20 min at 4 °C. The oxygen consumption rate (OCR) was measured with seven technical replicates for each mouse, as the following compounds were injected to final concentrations per well: ADP (4 mM), oligomycin (2.5 µM), carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone known as FCCP (2.0 µM) and antimycin A (1 µM) / rotenone (1 µM). OCR was measured in the absence or presence of etomoxir, with sequential addition of ADP (ATP precursor), oligomycin (complex V inhibitor), FCCP (a protonophore), and Rotenone/antimycin A . Four mitochondrial respiration states were calculated: basal respiration (respiration of mitochondria with substrates but without ADP), ATP production or phosphorylating respiration (rate of ATP formation from ADP and inorganic phosphate), proton leak or non-phosphorylating respiration (rate of oxygen consumption while ATP synthase is inhibited with oligomycin), and Maximal respiration state after the addition of FCCP. The instrument control, data analysis and file management were performed with the Agilent Seahorse Wave v2.6 software.The Palmitoyl-CoA oxidation depends on the activity of Carnitine palmitoyltransferase-1 (CPT-1), the rate-controlling enzyme for long-chain fatty acid oxidation. Measurements of CPT-1-mediated mitochondrial respiration was determined in mitochondria freshly isolated from mouse liver using the Seahorse XFe96 Extracellular Flux Analyzer (Agilent Technologies), as previously describedg for 30 min at 4 °C. The DNA was washed with 70% ethanol, and resuspended in 50 μL of molecular-grade water. 1.5 μl were used for subsequent qRT-PCR reactions with specific primers designed using Primer3web v4.1.0, within regulatory regions previously identified as BMAL1 binding sites in mouse liver, as reported in the ChIP-Atlas database163. Primer sequences are available in Supplementary Data 100–200 mg of liver tissue were homogenized with a pestle in PBS. Dual crosslinking was performed in a final volume of 1 ml using 2 mM of DSG  for 10 min at RT on a rotary shaker. DSG was washed out and a second crosslink was performed using 1% formaldehyde  in PBS for 15 min at RT on a rotary shaker. Crosslinking was stopped with 0.125 M glycine for 5 min at 4 °C. After two washes with ice-cold PBS, nuclei were isolated by resuspending the tissue in 600 μL of ice-cold nuclei preparation buffer  and incubating at 4 °C for 5 min in rotation. Nuclei were collected by centrifugation at 1,500 g for 12 min at 4 °C and, and resuspended in 600 μL of cold nuclear lysis buffer  for 30 min on ice. Nuclear lysates were stored at −80 °C. 300 μL of lysates were sonicated using a Bioruptor Pico Sonicator (Diagenode) for 15 cycles (30 s ON/30 s OFF). Chromatin fragments (100–500 bp) were evaluated on agarose gels using 10 μL of sonicated chromatin for DNA purification using the phenol method. 600 μL of ice-cold ChIP-dilution buffer  was added to the fragmented chromatin, and 10% volume was recovered as the Input. Immunoprecipitation was set up overnight at 4 °C, by adding 20 μL of magnetic beads  and a combination of two anti-BMAL1 antibodies: 1.25 μL rabbit anti-BMAL1  and 2.5 μL rabbit anti-BMAL1  in 900 μL final volume. Immunoprecipitations with 4 μL of normal mouse IgG  were performed simultaneously in 900 μL final volume. Sequential washes of the magnetic beads were performed for 10 min at 4 °C, as follows: Wash buffer 1 , Wash buffer 2 , Wash buffer 3  and TE buffer . Chromatin was eluted by adding 400 μL of fresh elution buffer  to the magnetic beads and incubating overnight at 65 °C. A treatment with RNase A at 0.1 mg/ml for 30 min at 37 °C was performed. The DNA was purified from the IPs and Inputs by adding one volume of phenol:chloroform:isoamamyl alcohol (25:24:1). After mixing and centrifugation, the aqueous phase was recovered, and DNA was precipitated by adding 1/10 volumes of sodium acetate (0.3 M pH 5.2), 20 μg of glycogen  and 2 volumes of ice-cold ethanol, at −80 °C overnight. DNA was pelleted by centrifugation at 12,000 × 164. Activity profiles were obtained averaging 5 consecutive days prior to the NAD+ treatment, and 5 consecutive days after the start of the treatment. Activity profile data from 30 min were averaged for statistical comparisons.Mice were individually housed in a light-tight, ventilated cabinet, under a 12 h light:12 h dark cycle, and ad libitum access to food and water. At the appropriate time for each treatment, animals were removed from their cages to receive IP injections for less than 2 min each. Cages were equipped with two infrared motion sensors . Beam break data were continuously recorded and compiled with the OASPAD20 (OMNIALVA) software, v2019, and files containing the number of beam breaks per 6-min bin were exported. Double-plotted actograms were generated using RhythmicAllyP < 0.05. GraphPad Prism version 8.4.2.679 for Windows  and Excel  were used for statistical analyses and plotting. 24-h period rhythms were assessed employing CircWave version 1.4165, CircWave uses a forward linear harmonic regression to calculate the profile of the wave fitted into a 24 h period. Daily rhythms were confirmed when the null amplitude hypothesis was rejected by running an F test that produced a significant value (P < 0.05). CircWave provides the calculation of the Centre of Gravity (CoG), representing the acrophase of the curve, with SD. Double-plotted data (ZT24) for visualization proposes are indicated in Figure legends, and were not included in the statistical analyses. Data from live mice were replicated in two independent experiments with similar results. Figures were assembled using Adobe Illustrator CC 2015 .All data were presented as the mean ± standard error of the mean, and two-way analysis of variance (ANOVA) followed by Tukey’s test for multiple comparisons was used for statistical analyses except when otherwise noted in the Figure legends. Differences between groups were rated as statistically significant at Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Sepsis is the most common cause of admission to intensive care units worldwide. Sepsis patients frequently suffer from sepsis-associated encephalopathy (SAE) reflecting acute brain dysfunction. SAE may result in increased mortality, extended length of hospital stay, and long-term cognitive dysfunction. The diagnosis of SAE is based on clinical assessments, but a valid biomarker to identify and confirm SAE and to assess SAE severity is missing. Several blood-based biomarkers indicating neuronal injury have been evaluated in sepsis and their potential role as early diagnosis and prognostic markers has been studied. Among those, the neuroaxonal injury marker neurofilament light chain (NfL) was identified to potentially serve as a prognostic biomarker for SAE and to predict long-term cognitive impairment. In this review, we summarize the current knowledge of biomarkers, especially NfL, in SAE and discuss a possible future clinical application considering existing limitations. Sepsis is a potentially life-threatening organ dysfunction caused by a dysregulated host response to severe infection , 2. Due Sepsis-associated encephalopathy (SAE) is one of the most common organ dysfunctions in sepsis and is also associated with significantly higher mortality rates , 16–18. Due to inconsistent diagnostic criteria in clinical studies and diverging daily clinical practice, the estimated prevalence of delirium in SAE varies from 9 to 71% in sepsis patients . As an eIn addition to acute changes in mental state during the acute phase, sepsis is associated with long-term cognitive dysfunction following hospital discharge. According to a study of long-term cognitive outcomes in patients admitted to ICUs with acute respiratory failure, septic or cardiogenic shock, 40% (after three months) and 34% (after twelve months) of patients had persisting cognitive impairment after hospital discharge . The sevIn the ICU, delirium severity is routinely measured using the CAM-ICU and Intensive Care Delirium Screening Checklist (ICDSC), both showing high reliability and validity in patients who are able to interact with the investigator. According to CAM-ICU, a patient is rated as delirious when (a) the mental status acutely changes or fluctuates and (b) the patient fails to pay attention and either (c) the patient exhibits disorganized thinking or (d) has an altered level of consciousness. To evaluate the level of consciousness, the Richmond Agitation-Sedation Scale (RASS) is used . AnotherThe major disadvantage of the CAM-ICU and the other scores is the reliance on patient interaction. Therefore, these clinical tools cannot be used in deeply sedated patients . Furthermore, patients with hypoactive delirium might also be underrecognized. Thus, the delirium rates in the ICU are likely to be underestimated in severely ill patients.Besides challenges in the practical use of delirium tests itself in SAE patients, a recent study evaluated the current practice of clinical SAE diagnostics in the ICU and demonstrated a great heterogeneity in the application of diagnostic tests in Germany .in-vivo data indicate neuronal and synaptic damage in sepsis alongside with activation of immune cells in the CNS. Analyses of brain autopsies from patients who died of sepsis showed diffuse cerebral damage with neuronal apoptosis, axonal damage and ischemic lesions ..21].Biomarkers should provide objective and quantitative results. In SAE, an ideal biomarker would provide high sensitivity and specificity independent of the effects of sedatives. It should enable an early diagnosis as well as a reliable outcome assessment. Several potential biomarkers have been proposed and studied in the course of SAE, such as neuron- and glia-derived proteins , 56, butIn the last decade, neurofilament light chain (NfL) has been intensively investigated as a biomarker in several neurological diseases . NfL is Neurofilaments are cylindrical proteins found in the neuronal cytoplasm . TogetheDue to its proven role as a biomarker directly reflecting neuronal damage with the opportunity of blood measurements, NfL has been considered an ideal candidate for the diagnosis and prognostic assessment in SAE . HoweverVery recently, in a gender and age-matched series of patients with community-acquired pneumonia we demonstrated that serum NfL levels were associated with the occurrence of SAE as determined by confusion or delirium but not with overall disease severity, thus supporting the specificity of Nfl as a marker for CNS involvement in infectious and inflammatory disease . These fConsidering these results, blood NfL levels could serve as a biomarker for SAE and may have potential to predict long-term cognitive impairment after sepsis. In addition to the evidence provided by the above-mentioned first studies with rather small sample size there are several arguments that NfL might have a larger potential as compared to other biomarkers. It has already proven to be directly associated with long-term cognitive impairment and even predicting worsening of cognition over time in other neurological diseases, such as multiple sclerosis, cardiac surgery, Alzheimers’ disease and mild cognitive impairment –101. MorTogether, an increase of NfL levels at a defined time point during sepsis or changes in serum NfL levels over time during SAE might be suitable and clinically useful to predict long-term cognitive outcome in patients with sepsis. An NfL increase might enable ICU clinicians to identify patients at high risk for structural brain damage and therefore to prioritize brain imaging and protection during the hospital stay , 56. TheThere are several factors that may influence serum levels of NfL in the setting of severely ill sepsis patients Fig. . Here, NIn general, NfL values show also interindividual variability in healthy individuals and in patients with certain comorbidities , 106. ThSeveral immunoassays  are currently available to reliably quantify NfL in blood at very low concentrations . To dateIn this regard, NfL might be valuable by providing information on long-term prognosis, as repeated NfL measurements might help to differentiate between reversible brain dysfunction  and structured brain damage  . FurtherIn 2001, Pepe and colleagues published a five-phase framework for cancer biomarker development, which has been modified by the Geneva Task Force for the Roadmap of Alzheimer’s Biomarkers for the development of biomarkers , 115. Ac