Breast milk alone is a poor and inadequate source of vitamin D. Many guidelines suggest supplementation of vitamin D to the newborns to prevent vitamin D deficiency. However, due to practices of outdoor breastfeeding and sunbathing, vitamin D supplementation may not be routinely required in our settings. Overzealous use of vitamin D supplementation and over‐the‐counter prescription may result in hypervitaminosis D. DMSA scan. Ergocalciferol (vitamin D2) and cholecalciferol (vitamin D3) are obtained from various dietary plant and animal sources, respectively. Moreover, cholecalciferol (vitamin D3) is also obtained from 7‐dehydrocholesterol present in the skin via the UV rays. Vitamin D₂ and D₃ through various enzymatic reactions in liver and kidneys get converted into 1, 25‐dihydroxy vitamin D. Vitamin D helps in the absorption of the calcium and phosphorus from the gut, and it also stimulates the normal mineralization and growth of bones. The recommended daily allowance (RDA) for individuals aged 0–12 months is 10 mcg per day (10 mcg = 400 IU), whereas the RDA for individuals aged greater than 1 year is 15 mcg (15 mcg = 600 IU).Breast milk is a notoriously poor source of vitamin D.2A 9‐month‐old male child was brought by his mother with complaints of excessive crying and decreased feeding for 2 days. There was no history of fever, cyanosis, noisy breathing, and chest retractions. She also gave a history of passing hard stool and frequent wet diapers. The child was conceived by in vitro fertilization and was first born among twins to a primigravida mother at 36 weeks of gestation with a birth weight of 2.05 kg. The other twin was female and had a birth weight of 2 kg. Postnatal period was uneventful. Both children were exclusively breastfed. General physical and systemic examinations were normal. Developmental milestones were appropriate for age. At the time of discharge, both twins were prescribed vitamin D supplements as many institutions are prescribing vitamin D in preterm breastfed infants as per American Academy of Pediatrics (AAP)/Indian Academy of Pediatrics (IAP) guidelines. Although the prescription for vitamin D was not available, on further evaluation, it was found that they were taking 800 IU of vitamin D daily from 1200 IU/mL dosage dropper with cumulative dose of more than 216,000 IU over 9 months.Hypervitaminosis D was suspected, and investigations were sent which showed raised serum creatinine, hypercalcemia, high serum vit D 25 OH) level, normal phosphate, and low parathyroid hormone level. Patient was managed with IV hydration to increase renal excretion of calcium. Table 5 OH leveAlthough both twins were taking the same doses of vitamin D for the same period of time, the second twin had no symptoms. She was also evaluated. However, her laboratory investigations were within normal limits Table  She was At 2‐month follow‐up, repeated investigations were normal. A DMSA scan was done which showed normal size kidneys with normal function and no cortical scarring Figure .3Vitamin D, a fat‐soluble vitamin synthesized in the human body via exposure to the sun, is frequently used as both prescription and over‐the‐counter formulation. The rare cases of vitamin D toxicity are usually secondary to misuse of over‐the‐counter supplements, erroneous prescriptions.The high doses ingested are stored in liver and adipose tissues and saturate vitamin D receptors. Although there is negative feedback regulation by an enzyme 25 hydroxylase in the liver, it is not inadequate to prevent the buildup of vitamin D metabolites such as 25(OH)D, 24,25(OH)2D, 25,26(OH)2D, and 25(OH)D‐26,23‐lactone, leading to abnormal calcium homeostasis.An average vitamin D content of human breast is around 22 IU/L that too in a vitamin D‐sufficient mother.In a study conducted in Nepal, vitamin D insufficiency [25(OH)D < 50 nmol/L] and deficiency (<30 nmol/L) prevalence were 3.6% and 0.6%, respectively, among newborn, in contrast to 59.8% and 14.0% among their mothers. The infants had adequate vitamin D status despite the mother being deficient.Vitamin D toxicity in an infant presents with nonspecific and subtle symptoms such as poor feeding, constipation, intolerance to food, polyuria, dehydration, lethargy failure to thrive, emesis, and even diarrhea. are direct effects of hypercalcemia, and frequently correlate with the calcium level. Our patient had constipation, irritability, and frequent diaper wetting. The vasoconstrictive effect of calcium may have hypertension in some patients, and hypercalciuria may lead to nephrolithiasis and nephrocalcinosis.The goal of treatment is to rehydrate the patient as hypercalcemia causes dehydration and decreases the calcium level in the body. Infusion of normal saline (10–20 mL/kg) can be used to expand the extracellular compartment. Before adding diuretics to increase urinary excretion of calcium, adequate urine flow must be established to prevent precipitation of calcium leading to dehydration and nephrolithiasis. The continual intestinal calcium absorption may lead to rise in calcium level despite treatment. Such cases can be managed by adding prednisolone to treatment regimen as it lowers calcium absorption by decreasing 1‐α‐hydroxylation of 25(OH)D to 1,25(OH)2D in the kidney. Other interventions include use of calcitonin, bisphosphonates, and pamidronate in resistant cases.4Overzealous use of vitamin D supplementation in newborns for prolonged duration increases the risk of hypervitaminosis. Although the guidelines suggest the use of vitamin D in newborns, the prevalence of vitamin D deficiency, practices of breastfeeding, and other sociocultural practices in the local settings should be taken into account before prescribing it.Sajal Twanabasu: Conceptualization; writing – review and editing. Jeevan Ghimire: Writing – original draft; writing – review and editing. Sushan Homagain: Writing – original draft; writing – review and editing. Prabin Duwadee: Writing – original draft; writing – review and editing. Susmita Devkota: Writing – review and editing. Ashish Bhandari: Writing – review and editing. Prabhu Sunar: Writing – review and editing.None.None.Written informed consent was obtained from the parents to publish this report in accordance with the journal's patient consent policy.


Despite the confirmed association between higher BMI with increased risk of the acute respiratory distress syndrome (ARDS), the association between obesity with mortality in critically ill patients with coronavirus disease 2019 (COVID‐19) is not clear. The present study aimed to investigate the association between obesity with treatment duration, ICU length of stay, and the risk of death in critically ill patients with COVID‐19.This case–control study was performed on 223 patients with COVID‐19 including 148 surviving patients as the control group and 75 eventually dead patients as the case group in Rasht, Iran. Data on demographic factors, comorbidities, anthropometric measurements, the length of hospitalization and the mortality were obtained from patients' medical records.p = .04), but not with BMI after adjustments for age, gender, length of stay in ICU, chronic diseases and smoking. The results did not change after further adjustments for biochemical and pathological factors.The mortality rate was significantly associated with weight , but not with BMI after adjustments for age, gender, length of stay in ICU, chronic diseases and smoking. The results did not change after further adjustments for biochemical and pathological factors.The mortality rate was significantly associated with weight (OR = 1.04, 95% CI: 1.002–1.083,  A 1:2 case to control ratio was used to calculate sample size with 95% CI, 80% power, and the odds ratio of a similar previous studyThis case–control study was conducted on 223 patients with COVID‐19, admitted to Razi Hospital from April to July 2019 in Rasht, Iran. The inclusion criteria were a confirmed diagnosis of COVID‐19 by a CT scan test and lung involvement, hospitalized in ICU for at least 24 h in a certain period (3 months from baseline). The exclusion criteria were age lower than 18 years, patients who died for other reasons except for COVID‐19, patients who did not have the required information in their medical files. Patients with COVID‐19 who died within 1 month of hospitalization were assigned to the case group , co‐morbidities, pregnancy status and other personal information were obtained from the patient's medical files. The length of hospitalization and the mortality rate were investigated for all participants. The height (cm) and weight (kg) that were documented upon admission, either by self‐report or obtainment by nursing staff were used for BMI calculation as an indication of healthy bodyweight. The other clinical information of participants, including the report of lung CT scan and co‐morbidities including diabetes, respiratory diseases, transplantation and dialysis, were obtained from the patient's medical files. Also, in the control group who recovered and discharged, the required information was obtained from their medical files and, in the case of lack of knowledge, an oral interview was conducted in person or by telephone.2.2T‐test and the chi‐squared test were used for comparison of the quantitative and qualitative variables between the groups, respectively. Different models of bi‐nominal logistic regression were used to investigate the association between COVID‐19 with weight and BMI and adjusting the effect of the confounding variables. The odds ratio (OR) and 95% CI were reported. All statistical analyses were SPSS statistical software , and a p‐value <.05 was considered significant.The independent 3p = .003) and smoking  compared to the dead patients. There was no significant difference on the length of stay in the ICU between the case and control groups , HCO3 level (25.72 ± 4.81 mEq vs. 20.82 ± 12.15 mEq), PTT (33.57 ± 11.43 vs. 40.31 ± 16.55 s), GCS (14.34 ± 1.98 vs. 7.83 ± 1.08) compared to the controls (All p < .05) , SGPT (30.75 ± 32.24 vs. 80.71 ± 171.34 1 U/L), SGOT (39.56 ± 28.09 U/L vs. 87.58 ± 165.53 U/L), the MAP (71.86 ± 12.16 mm Hg vs. 59.61 ± 6.88 mm Hg), PCOp = .04) (Model 1). The result remained significant after additional adjustments for biochemical and pathological factors  (Model 2). Regarding the association between mortality and obesity, there was no association between the mortality rate with BMI after adjustment for age, gender, length of stay in ICU, chronic diseases and smoking  (Model 1) and after additional adjustments for biochemical and pathological factors  (Model 2).As shown in Table 4The present study found a positive association between body weight and mortality in patients with COVID‐19. No significant association was found between BMI and the risk of death in critically ill patients with COVID‐19. Few studies were performed on the association between obesity and clinical outcomes of critically ill patients with COVID‐19. In line with the results of the present study, some previous studies showed that body weight was associated with unpleasant complications in patients with COVID‐19.In contrast with the results of the present study, several studies reported that higher BMI is positively associated with higher risk of COVID‐19 complications.The exact mechanism of the effect of bodyweight on COVID‐19 is not yet clear. Several studies that have suggest various mechanisms.Further studies are required considering the limitations of this study, including the small size of the sample, the different medical backgrounds of the patients, to confirm these findings and to identify the underlying mechanisms of the effects of bodyweight on COVID‐19.5The results from this case–control study indicate that higher weigh, not BMI, is associated with higher mortality in critically ill patients with COVID‐19 after controlling for confounding variables. Weight loss may reduce COVID‐19 severity and hospitalization. These findings need to be confirmed by future large‐scale studies that include patients of various pathological backgrounds. In addition, further studies should consider the patient's body composition, such as fat mass, to establish the relationship between obesity and COVID‐19 outcomes. If the results of this study are confirmed in future research, weight loss diets in obese or overweight people can be recommended as a strategy to prevent the exacerbation of the symptoms of COVID‐19.Mahdi Mousavi: Software . Zahra Salimi: Validation . Asma Rajabi: Investigation . Mobina Zeinolabedin: Formal analysis . Amin Barzkar: Validation . Zahra Mahmoudi: Validation . Farkhondeh Alami: Validation . Samaneh Mirzaei Dahka: Methodology . Somayeh Gholami: Writing – original draft . Masoume Rahvar: Software . Masoume Pourtaleb: Methodology . Sara Khoshdooz: Visualization . Maryam Gholamalizadeh: Formal analysis . Mohammad Attari: Methodology . Saeid Doaei: Formal analysis . Soheila Shekari: Validation . Naser Kalantari: Methodology .This study is financially supported by the Student Research Committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran (Code 1399/63396). Thanks to all the colleagues in the research committee for their nice cooperation.The authors declare no competing interests.The protocol of this study was approved by the ethics committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran (Code: IR.SBMU.RETECH.REC.1399.1057).Written consent was obtained from the participants or their first degree relatives (for deceased patients) to participate in the study.


Long-term observations have shown that many plants and aboveground animals have changed their phenology patterns due to warmer temperatures over the past decades. However, empirical evidence for phenological shifts in alpine organisms, particularly belowground organisms, is scarce. Here, we investigate how the activities and phenology of plants, soil microbes, and soil fauna will respond to warming in an alpine meadow on the Tibetan Plateau, and whether their potential phenological changes will be synchronized. We experimentally simulate an increase in soil temperature by 2–4 °C according to future projections for this region. We find that warming promotes plant growth, soil microbial respiration, and soil fauna feeding by 8%, 57%, and 20%, respectively, but causes dissimilar changes in their phenology during the growing season. Specifically, warming advances soil faunal feeding activity in spring and delays it in autumn, while their peak activity does not change; whereas warming increases the peak activity of plant growth and soil microbial respiration but with only minor shifts in their phenology. Such phenological asynchrony in alpine organisms may alter ecosystem functioning and stability. Phenological shifts driven by climate change are well-studied in plants and aboveground animals, but scarcely in belowground biota. Here, the authors show that soil warming causes phenological mismatches between plants, soil microbes and soil microarthropods in an alpine meadow. Phenological changes in biological activities, in turn, may further have feedback effects on the climate6. Warming has been reported to have particularly strong effects on ecosystems at high elevations and altitudes8, where ecological processes are rather sensitive to environmental changes and strongly limited by cold temperatures10. Warming, however, can alleviate the thermal constraints12, thus biological activities are expected to increase under predicted future warming scenarios. Further, the effects of warming on these biological activities may depend largely on soil moisture13. Water scarcity during the dry summer months could be a limiting factor that offsets or diminishes the increases in biological activities caused by warming14.Global warming may not only change the biodiversity and community structure of both above- and below-ground organisms, but also their activity and phenology, including changes in the timing and magnitude of activity peaks17. In contrast to extensive recent work on the phenology of plants and aboveground animals in the context of climate warming24, soil biota have rarely been included in phenological studies, despite their high sensitivity to environmental changes1. In cold biomes, for example, long-term records have shown spring advancement and/or autumn postponement in plant growth in response to warming25. The resulting longer plant growing season may increase gross primary productivity and net CO2 uptake26, associated with a negative feedback to climate change6. However, whether similar phenological shifts may also occur in soil biota, and whether the phenology of different groups of organisms  would respond in synchrony to warming in a given region or ecosystem, remains largely unknown. Therefore, warming experiments on above-belowground communities could provide novel insights into this topic.Since temperature is one of the main drivers of the phenological cycle of organisms, on-going climate change may alter biological phenology with consequences for ecosystem functioning27 using whole-soil-profile heating rods. Based on PhenoCam-derived Normalized Difference Vegetation Index (NDVI) and in-situ investigations (combined with the dynamic chamber method and bait-lamina method), the seasonal dynamics of plant growth (represented by NDVI data), soil microbial respiration (represented by root-free soil respiration), and soil fauna feeding  were investigated to explore phenology shifts in the context of continuous warming. We tested the following hypotheses: H1: Experimental warming increases biological activities  during the growing season, due to warming-induced releases of low-temperature constraints12. H2: The effects of experimental warming on biological activities would vary over the growing season. More specifically, the increases in biological activities would be particularly pronounced in the early and late-growing seasons, resulting in earlier spring phenology and delayed autumn phenology. Such a phenological shift pattern of alpine plant growth in response to natural climate warming has been revealed over the past few decades10.Here, we used a warming experiment in an alpine meadow of the Tibetan Plateau Fig. , where w14. By contrast, for the alpine meadow we studied, the growing season is characterized by wet and cool climatic conditions. During the growing season, the heated plots achieved a warming of about 2.2 °C at 5 cm soil depth  and increases in the maximum values of plant growth30. Warming may have led to increased water stress during the middle and/or late alpine growing season31. Consistent with this mechanism, we found a significant reduction in soil water content during the late-growing season  across the plots. These analyses showed that the activities of soil fauna and particularly plants and soil microbes were all positively correlated with soil temperature but not with soil water content : 72.9 ± 17.6 g kg−1, extractable organic carbon (EOC): 433.6 ± 38.3 mg kg−1, total nitrogen (TN): 7.2 ± 1.6 g kg−1, extractable total nitrogen (ETN): 62.7 ± 6.8 mg kg−1, inorganic nitrogen (IN): 7.8 ± 0.9 mg kg−1, ammonium nitrogen (NH4+-N): 4.2 ± 0.5 mg kg−1, nitrate nitrogen (NO3−-N): 3.6 ± 0.8 mg kg−1, and pH value: 7.6 ± 0.1 in the surface soil (0–10 cm) of experimental plots.We conduced our study at the Haibei Alpine Grassland Ecosystem Research Station, utilizing a whole-soil-profile warming experiment in an alpine meadow on the Qinghai-Tibetan Plateau, China . The climate of this study area is a continental monsoon characterized by short, cool summers and long, cold winters, with a 6-month growing season (from mid-April to mid-October). The average annual air temperature is about 1.1 °C, and the highest temperature occurs in July or August, and the lowest temperature occurs in January. Average annual precipitation over the last three decades was about 485 mm, with most annual precipitation (84%) falling from May to SeptemberThe experiment consists of four blocks. Each block contains two circular plots (with a diameter of 3.5 m), one of which is subjected to warming climate, while the other one is under ambient climate (Control); that is, 4 replicated plots for each climate treatment see Fig. , to achiAlthough warming may inevitably affect precipitation patterns, we did not manipulate the precipitation in this study due to inconsistent precipitation projections for this region. The experimental plots were established in 2017, and the warming treatments began on June 18, 2018. We conducted this study in the growing season of 2021 (from May to October), i.e., 3 years after the beginning of the experiment. We measured soil temperature and soil water content by a custom-made thermistor and a PR2/6 sensor  at 10-min intervals, respectively. The corresponding data are shown in Supplementary Fig. 10. Specifically, we adopted the technique of repeated digital photographs for plant growth activity and its phenology monitoring  the PhenoCam  obtained one pair of RGB and RGB + NIR images per shot48. Digital Number (DN) values  of red, green and blue channels were extracted from RGB images; DN values [(RGB + NIR)DN] of red, green, blue, and near infrared channels were extracted from RGB + NIR images. (2) The DN values extracted from RGB and RGB + NIR images were respectively calibrated to RDN_E, GDN_E, BDN_E, and (RGB + NIR)DN_E according to the exposure value49. (3) After calculating the calibration value of NIRDN_E, phenological camera NDVIC was calculated50: C to normal NDVI values using a linear regression  and digital number-derived NDVIC during the growing seasons of 2018 and 2019 . Based on the daily dynamics of NDVI  function to obtain the smooth seasonal dynamic curves of NDVI, and to interpolate the missing values54. For better understanding, the comparison for the raw, filtered, and fitted NDVI sequences under ambient and warming plots is shown in Supplementary Fig. In order to establish high quality NDVI time series, we conducted preprocessing procedures for the obtained normal NDVI values. First, we deleted 0- and negative values  to a depth of 65 cm in line with previous studies57. Specifically, we inserted a PVC collar  into the bulk soil (65 cm depth) of each plot in June 2016. This method has been demonstrated to be efficient in cutting off old plant roots and prevented new roots from growing inside the tubes59. We used a SF-9000 soil CO2 efflux analyzer  attached to a chamber to measure soil CO2 efflux twice (for the mean) on a sunny day from 8:00 to 11:00 at the beginning of each month of 2021 growing season .The soil is a clay-loam, and its average thickness is about 100 cm (with very little SOC below 70 cm); we therefore measured soil microbial respiration (μmol CO60. Standard bait-lamina strips (1 mm thick × 6 mm wide × 120 mm long) with 16 holes  were made from PVC rigid plastic . We filled the holes with a bait substrate consisting of 70% cellulose powder, 27% wheat bran, and 3% activated carbon. The bait substrate is primarily consumed by earthworms, isopods, millipedes, enchytraeids, Collembola, and oribatid mites; whereas microbes contribute very little to the perforation and loss of bait substrate in a short period of study34.We measured soil fauna activity to a depth of 10 cm using standard bait-lamina strips34. The score of bait consumption can therefore range from 0 (no feeding activity) to 16 (maximum feeding activity) per strip. We calculated the mean bait consumption of the six strips per plot prior to statistical analysis.To insert the strips into the soil without dislodging the bait in the holes, or breaking the strips, we used a steel knife to create a slit 10 cm deep, with the same width and thickness as the bait-lamina strips. We inserted six strips into the middle of each plot at a distance of 10 cm from one another to account for some potential spatial heterogeneity. We placed strips in the middle of each month during the growing season of 2021 . After 30 days of exposure, we gently removed all bait-lamina strips from the soil (and subsequently replaced with new bait strips), and immediately assessed the perforation in the field. Specifically, we carefully inspected all holes  and scored them as 0 (no feeding activity), 0.5 (intermediate feeding activity), or 1 (high feeding activity)−2). As 96% of root biomass is typically found in the upper 30 cm61, we took five soil cores  in each plot to evaluate root biomass (g m−2). Total plant biomass (g m−2) is equal to the sum of shoot biomass (g m−2) and root biomass (g m−2).Mid-August 2021, we randomly chose two 50 cm × 50 cm quadrats in each plot to investigate aboveground productivity, and clipped all plants of the two diagonal 25 cm × 25 cm sub-quadrats in each quadrat to evaluate shoot biomass , and the extracting solution was then filtered through a 0.45 mm filter membrane. The extractable organic carbon in the filtered solution was analyzed by a TOC/TN analyzer . We calculated soil MBC as the difference in extractable C concentrations between fumigated and unfumigated samples using a conversion factor of 0.45.Mid-August 2021, we randomly collected five soil samples  from each plot and evenly mixed them (as a composite sample for this plot) to determine the content of microbial biomass carbon by the chloroform fumigation-extraction method63. Note that this method is limited to the extraction of (micro)arthropods, but other taxa  are also important for ecological processes such as decomposition. We used a VHX-Digital microscope to classify, count and measure the body length of all observed individuals that were dominated by Collembola and Acari. We calculated individual mean biomass  using the formula L as the individual mean body length (μm), with Collembola: a = 1.34, b = 1.99; Oribatida: a = 2.12, b = 2.71; Prostigmata: a = 2.12, b = 2.864. Total biomass is equal to the number of individuals (abundance) multiplied by the individual mean biomass.Mid-August 2021, we collected two soil cores  from each plot and placed them together (as a composite sample for this plot) to extract soil fauna using a Kempson methodlmer  function in R “lme4” package65. In the models, plot nested within block served as a random effect. We performed the Shapiro-Wilk test and Levene’s test to test the normality of the model residuals and the homogeneity of variance, respectively. If not normally distributed, the data were log-transformed log(x + 1) and the analyses were redone. Further, we conducted the orthonormal contrasts to test for the differences in biological activities among three different stages of the growing season  using the glht  function in R “multcomp” package. In the models, we performed FDR  corrections for multiple comparisons of means to reveal significant differences (p < 0.05) among respective groups. Additionally, we used the same linear mixed models to test for the differences in soil temperature and soil water content between ambient and warmed plots for each month. Finally, we built the same linear mixed model with the same random effects structure to test the correlations (1) among biological activities, and (2) between each biological activity and abiotic factors . We performed all statistical analyses using R version 4.2.266 with R studio interface.We used linear mixed effects models  to analyze the fixed effects of climate (ambient vs. warming), sampling time , and their interactions on biological activities  and other potential explanatory variables  using the Further information on research design is available in the Supplementary Information FilePeer Review FileReporting Summary


Risk stratification of kidney cancer patients after nephrectomy may tailor surveillance intensity and selection for adjuvant therapy. Transcriptomic approaches are effective in predicting recurrence, but whether they add value to clinicopathologic models remains unclear.Data from patients with clear cell renal cell carcinoma (ccRCC) was downloaded from The Cancer Genome Atlas. Clinicopathologic variables were used to calculate SSIGN  scores. The 16 gene recurrence score (RS) signature was generated using RNA‐seq data. Transcriptomic risk groups were calculated using the original thresholds. SSIGN groups were divided into low, intermediate, and high risk. Disease‐free status was the primary endpoint assessed.p = 0.05). By risk groups, RS further risk stratified the SSIGN intermediate‐risk group . SSIGN intermediate‐risk patients with low and high RS had a 3‐year recurrence rate of 8.0% and 25.2%, respectively. Within this risk group, the area under the curve (AUC) at 3 years was 0.69 for SSIGN, 0.74 for RS, and 0.78 for their combination.SSIGN and RS were calculated for 428 patients with non‐metastatic ccRCC. SSIGN low‐, intermediate‐, and high‐risk groups demonstrated 2.7%, 15.2%, and 27.5%, 3‐year recurrence risk, respectively. On multivariable analysis, the RS was associated with disease‐free status (sub‐distribution hazard ratio (sHR) 1.43 per 25 RS [95% CI (1.00–1.43)], Transcriptomic recurrence scores improve risk prediction even when controlling for clinicopathologic factors. Utility may be best suited for intermediate‐risk patients who have heterogeneous outcomes and further refinement for clinical utility is warranted. Although multiple nomograms such as the Memorial Sloan Kettering Cancer Center nomogram, Stage, Size, Grade and Necrosis score (SSIGN) and the University of California Integrated Staging System (UISS)Genomic biomarkers have the potential to improve risk stratification as well as further refine treatment selection and are already used across several cancer types such as breast and prostate to help inform routine clinical management such as administering adjuvant therapy.22.1http://www.cbioportal.org/) and the Genomic Data Commons bioportal . The dataset contains outcome data linked to detailed clinical information, somatic mutations, and mRNA expression counts as previously described. Eligible patients for analysis included those with (1) clear cell renal cell carcinoma (2) pathological stage I‐III and (3) complete clinicopathologic data. Patients were excluded from the analysis if they were (1) pathological stage IV, (2) metastatic or (3) non‐clear cell renal cell carcinoma. The Institutional Review Board deemed the study exempt because the study did not constitute human participant research and used a publicly available dataset.The TCGA Pan RCC data was downloaded from cbioportal for Cancer Genomics .2.3Continuous and categorical clinical and pathological variables were reported as medians  and counts (proportions), respectively. Comparisons of clinical and pathological variables between RS risk groups were performed using the Kruskal–Wallis test for continuous variables and chi‐squared tests for categorical variables. Fine and Gray models were used to determine the associations between time to recurrence and clinical and pathological characteristics, RS, and SSIGN, with death before recurrence treated as a competing risk.3p < 0.001). Patients within the RS high group had a higher frequency of Furman grade 4 disease  and lymph node‐positive disease . Patients classified RS high were also more likely to be classified as high SSIGN .Baseline clinical and pathological characteristics of the 428 patients with non‐metastatic ccRCC are presented in Table Disease recurrence cumulative incidence plots were generated for RS and SSIGN. Median follow‐up time for non‐recurring patients was 43 months and overall recurrence rate was 16.8%. As expected, higher risk groups have greater recurrence risk  and RS  were associated with increased rates of disease recurrence (Table p < 0.001). Compared with the SSIGN low‐risk group, both SSIGN intermediate‐ and high‐risk groups were associated with an increase in recurrence rates . Tumor stage, tumor size, and lymph node involvement were also significantly associated with recurrence  and RS  remained significantly associated with recurrence when analyzed as a continuous variable , while only the high‐risk RS group was significantly associated with recurrence . Discrimination for the recurrence endpoint was examined using ROC curve analysis for RS, SSIGN, and the combination of SSIGN plus RS. The combination of SSIGN and RS performed the best in this analysis with an area under the curve (AUC) of 0.80 followed by SSIGN alone with an AUC of 0.78 and RS with an AUC of 0.70  model, both SSIGN score  and categorical RS  were both significantly associated with recurrence in SSIGN intermediate patients. The ROC curves for RS combined with SSIGN, SSIGN and RS had AUCs of 0.78, 0.69, and 0.74 respectively in this risk group  score and the second using a 16 gene assay, were shown to be correlated with developing recurrences even when controlling for clinicopathologic characteristics. However, major limitations of the CCP cohort included low recurrence rates, inclusion of low‐grade patients, and non‐clear cell histology in the cohort. The second classifier utilized a 16 gene signature that was developed in a cohort from Cleveland Clinic and validated in patients from Hôpital Foch and Hôpital Necker Enfants Malades. A second validation study using an independent cohort was then performed using patients who were enrolled in the S‐TRAC clinical trial. In this study only stage III kidney cancer patients were included,In this current study, we validated the utility of the RS in a heterogeneous, non‐single center cohort from the TCGA. We found that RS was prognostic as a continuous variable, even when controlling for clinicopathologic variables. As expected, worse RS also correlated with adverse clinical characteristics. Although patients can be risk stratified by routine clinicopathologic variables using SSIGN and other classifiers, there are only modestly accurate and there is room for improvement with a genomic classifier. This is especially true among clinically intermediate‐risk patients who have very variable prognoses. In general, most low‐risk RS patients will not recur and most high‐risk RS patients have enough risk where they will be offered adjuvant therapy regardless of classifier. With a modest number of events, its often the intermediate‐risk group that has enough heterogeneity in biology and outcome where genomic classifiers may play a large role. Such is the case in prostate cancer, where intermediate‐risk cancer is frequently separated into favorable and unfavorable subsets leading to divergent therapeutic treatments.With significant advances in systemic therapy, contemporary management of high‐risk localized disease will likely involve using systemic therapy earlier in the disease course. Genomic information harbored from the primary tumor has the potential to help better predict recurrence and can thereby, improve outcomes of patients by allowing for earlier escalation of treatment. Similarly, there is growing interest in utilizing transcriptomic signatures to help select treatment in metastatic clear cell kidney cancer. Discerning tumors with high angiogenesis gene expression and high immune gene expression can potentially help clinician navigate which tumor mechanism to target with systemic therapy.Our study is not without limitations. The study is retrospective in design and therefore validation in prospectively enrolled well‐annotated cohorts is necessary. Although the TCGA includes patients from multi‐institutions, the data may not be representative of the general population,5Transcriptomic recurrence scores can help improve risk stratification of patients with intermediate SSIGN clinical risk patients. These patients have similar risk of recurrence as clinically high‐risk patients. Transcriptomic signatures can be combined with clinical information to improve risk assessment and further evaluation of clinical implementation is warranted.The authors confirm contribution to the paper as follows: study conception and design: B. Shuch, E. Davicioni, J. Proudfout, A. Ibarra; data collection: N. Patel, A. Hakansson, P. Muraki, S. Ohtake; analysis and interpretation of results: B. Shuch, E. Davicioni, A. Hakansoon, K. Chamie, A. Pantuck, Y. Liu, V. Liu, N. Patel; draft manuscript preparation: all authors. All authors reviewed the results and approved the final version of the manuscript.Veracyte, Inc.A. Hakansson, J. Proudfout, Y. Liu, L. Webber, A. Ibrarra, V. Liu, E. Davicioni are employees of Decipher Biosciences, a subsidiary of Veracyte, Inc. B. Shuch receives consulting fees from Veracyte, Inc.The Institutional Review Board deemed the study exempt because the study did not constitute human participant research and used a publicly available dataset (UCLA IRB#22‐000680).Figure S1Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.


Little is known about the association between liver indicators , and fatty liver index (FLI)) and cancer development in patients without preexisting liver disease.We conducted a retrospective cohort study with participants who underwent voluntary health checkups and without fatty liver between 2005 and 2018. Our primary outcome was the development of any type of cancer, and its association with each liver indicator was evaluated.A total of 69,592 participants  were men) were included. During a median follow‐up of 5.1 years, 3779 (5.4%) patients developed cancer. Compared to participants with a low NFS, those with a medium NFS had a higher risk of developing any type of cancer , whereas those with a medium FIB‐4 index had a decreased risk of developing any type of cancer compared to those with a low FIB‐4 index . Patients with higher scores tended to have a higher risk of digestive organ cancer, regardless of the indicator. A high FLI was also associated with an increased risk of breast cancer ; however, those with a medium FIB‐4 index  and NFS  had decreased risks of developing breast cancer compared to those with a high FIB‐4 index and NFS, respectively.Among patients without fatty liver, a higher liver indicator score was associated with an increased risk of cancer in the digestive organs, regardless of the indicator. Notably, those with a medium FIB‐4 index or NFS had a lower risk of developing breast cancer, whereas those with a medium FLI had an increased risk. p < 0.01).The cumulative incidences for the development of any type of cancer based on the index score groups for each index. The differences of cumulative incidence based on the FIB‐4 index were the most obvious, but all indexes were significantly associated with cancer development (all  For example, chronic hepatitis C virus infection is a risk factor for pancreatic cancer and non‐Hodgkin lymphoma.Although liver biopsy is one of the most accurate approaches for evaluating liver injury, is invasive and expensive. Therefore, less invasive indicators for evaluating liver injury have been developed, including the FIB‐4 index, NAFLD fibrosis score (NFS), and fatty liver index (FLI). The FIB‐4 index and NFS were developed as indicators of liver fibrosis in patients with hepatitis C virus infection2A retrospective cohort study was conducted at the St. Luke's International Hospital, Tokyo, Japan using the St. Luke's Health Checkup Database. All participants who visited the Center for Preventive Medicine at the hospital underwent voluntary health checkups between 2005 and 2018 were included in the study. We excluded those who had a prior history of any type of cancer during the baseline health checkups. Because we focused only on patients who did not have any liver disease, including fatty liver, as discussed above, those with severe liver disease, such as liver cirrhosis, or fatty liver at baseline based on the findings of abdominal ultrasonography as a part of the health checkups were also excluded. As a part of health checkups, all participants underwent abdominal ultrasounds to evaluate abdominal disease, including fatty liver. Based on the finding in initial abdominal ultrasound, we excluded participants with fatty liver. As a result, scores in each liver fibrosis index including FLI, may be lower than general population. Participants were followed‐up either when they visited to the center for health checkups voluntarily or visited to the hospital for other reasons than health checkups by the end of 2018. The time from baseline date to the last follow‐up date was calculated and used for time variable for survival analyses. Once participants developed any type of cancer, the follow‐ups were terminated at the time of diagnosis. Therefore, there was no reverse causality for the association between liver injury and subsequent cancer development. Our primary outcome was the development of any type of cancer in the follow‐up period. Outcomes were evaluated based on liver fibrosis indexes. The St. Luke's Ethics Committee Institutional Review Board approved this study .2.1Our primary outcome was the development of any type of cancer during follow‐up, and the secondary outcome was the development of each type of cancer based on the International Classification of Diseases, 10th revision. Information about cancer development was obtained from participants' self‐reports and their electronic medical records at the hospital or the hospital's cancer registry. As part of the health checkups, all participants were asked about their current and past histories, including cancer development, at every visit. In addition, information about cancer diagnoses made by each physician at the hospital was obtained from electronic medical records. Information about cancer diagnoses made in hospitals other than St. Luke's was collected from the cancer registry at the hospitals or referral documents from other hospitals. We have combined this information with our outcomes.2.22), normal weight (>18.5 to ≤24.9 kg/m2), and overweight/obese (>24.9 kg/m2). Based on this information at baseline, the FIB‐4 index,As a part of the health checkups, all participants reported their demographic information (age and sex) and medical history (diabetes), blood tests were performed (liver enzymes , serum albumin, triglyceride, and platelets), and height, weight, and abdominal circumference were measured. Body mass index (BMI) was calculated and categorized based on the World Health Organization's Asian criteria, as follows: underweight (<18.5 kg/m0.5)FIB‐4 index = (AST × age)/(platelet count × ALT2) + 1.13 × impaired fasting glucose/diabetes  + 0.99 × (AST/ALT) − 0.013 × platelet count (×10 NFS = −1.675 + 0.037 × age (years) + 0.094 × BMI (kg/mount ×10 9 /L − 0FLI = exp(0.953 × log(triglycerides) + 0.139 × body mass index +0.718 × log(γ‐GTP) + 0.053 × abdominal circumference − 15.745)/(1 + exp(0.953 × log(triglycerides) + 0.139 × BMI + 0.718 × log(γ‐GTP) + 0.053 × abdominal circumference − 15.745)) × 100.Based on these indexes, we categorized participants into three groups: low , medium , and high .2.3As potential confounders for the association between each index and outcomes, we obtained the participants' demographic information, social histories, and medical histories based on questionnaires administered in the health checkup. Social histories included the participants' smoking status , alcohol consumption status , and exercise habits . In addition, medical histories, including diabetes, hypertension, and family history of any type of cancer, were obtained. Models driven by each index were compared using C‐statistics.2.4We summarized the participants' characteristics separately based on the score for each index. Then, cumulative incidences for subsequent development of any type of cancer were drawn based on the score  for each index, and the log‐rank test was performed to identify significant differences. Finally, the Cox proportional hazards model was applied to evaluate the association between each index and outcomes after adjusting for potential confounders.All analyses were performed using Stata MP 16.2 in 2022 (STATA Corp.).3n = 5377), those with prior severe liver disease (n = 95) and those with a lack of laboratory measures at baseline (n = 3), 69,592 participants were included in this study . Table A median duration of follow‐up of the participants was 5.1 years (interquartile range: 2.1–9.7 years) and a median number of follow‐ups was 5 times (interquartile range: 3–11 times). During the period, 3779 (5.4%) patients developed any type of cancer. Figure Table 4Participants with medium NFS tended to have a higher risk of developing any type of cancer than those with low NFS. By contrast, patients with medium FIB‐4 index tended to have a lower risk of developing any type of cancer than those with low FIB‐4 index. With regard to specific cancer types, patients with high FIB‐4 index had a significantly higher risk of developing oropharyngeal and digestive organ cancers, including hepatocellular carcinoma. A high FLI was also associated with the development of digestive organ cancer. Notably, participants with high FLI had higher risk of developing breast cancer; however, those with medium FIB‐4 index or NFS had lower risk of developing breast cancer.Our results partially correspond to those of previous studies, even though our target population, individuals without fatty liver, and target cancer types were different from those of previous studies. For example, the FIB‐4 index has been reported to be related to hepatocellular cancer development in patients with liver disease, such as those with chronic hepatitis C virus infection without sustained virological response after interferon‐based therapy,We hypothesized that the differences we observed in the direction and magnitude of the associations between the score for each indicator and the development of any or a certain type of cancer may result from differences in the origin and purpose of the indicators. The FIB‐4 index and NFS mainly include hepatobiliary markers, such as AST, ALT, and platelet count, and thus reflect the degree of liver fibrosis. When the liver is injured, apoptotic or necrotic hepatocytes, activated Kupffer cells, hepatic stellate cells (HSCs), and neutrophils produce reactive oxygen species.Our study found that a high FLI was associated with the development of breast cancer; by contrast, a medium FIB‐4 index and NFS were associated with a lower risk of developing breast cancer. Although the underlying mechanism for this finding is unclear, estrogen levels might play a role. Several studies have suggested an association between estrogen deficiency and liver fibrosis, which can be evaluated using the FIB‐4 index or NFS. For example, among patients with chronic hepatitis C virus genotype 1b infection treated with interferon‐based therapy, those with biopsy‐confirmed liver fibrosis had lower estrogen receptor alpha expression than those without fibrosis,This study had several strengths. First, it included a large population of approximately 70,000 participants. This large sample size provided sufficient power for analysis. In addition, our study considered several indicators, including the FIB‐4 index, NFS, and FLI. This information would be useful for examining the mechanisms underlying the study results.However, our study also has several limitations. First, our study included patients without fatty liver who had relatively low liver fibrosis indexes. The number of participants with high indexes in our study was relatively small, which potentially reduced the statistical power in the high score category. In this context, different cutoff values of these indicators may be considered for individuals without fatty liver to more accurately evaluate the association between the score category of each indicator and cancer development. Second, some participants may have had existing occult cancer at baseline. However, based on the cumulative incidence of cancer, the number of patients with undiagnosed cancer at baseline was limited. Third, our exclusion criterion regarding fatty liver was based on the findings of abdominal ultrasonography, although the definitive diagnosis of fatty liver requires liver biopsy.5Among individuals without fatty liver, those with a medium NFS had a higher risk of developing any type of cancer, whereas those with a medium FIB‐4 index had a lower risk of cancer development. With regard to development of individual cancer types, a higher score was associated with the development of cancer in the digestive organs, regardless of the indicator. However, individuals with a medium FIB‐4 index and NFS had a lower risk of breast cancer, whereas those with a high FLI had an increased risk of developing breast cancer.Hiroshi Ito: Conceptualization (supporting); writing – original draft ; writing – review and editing (supporting). Takeshi Kimura: Methodology (supporting); supervision (supporting); writing – review and editing (supporting). Takuro Shimbo: Methodology (supporting); supervision (supporting); writing – review and editing (supporting). Michiaki Higashitani: Conceptualization (supporting); project administration (supporting); writing – review and editing (supporting). Kazuki Yamamoto: Supervision (supporting); writing – review and editing (supporting). Daiki Kobayashi: Conceptualization (lead); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (lead); resources (lead); software (lead); validation (lead); visualization (lead); writing – review and editing (lead).None.None.


The thermal deformation behavior of the Mg–Gd–Y–Zr–Ag alloy was studied by isothermal hot compression tests at high temperatures. The flow stress increased with increased strain rates and decreased temperatures, first increasing and finally remaining stable with increased strain. A hot processing map was built. Using the processing map and microstructural analysis, the temperature should remain at 673–773 K for this alloy to ensure the deformation quality. The primary softening mechanism is discontinuous dynamic recrystallization (DDRX). Rising temperatures and declining strain rates facilitated the emergence and growth of Dynamic recrystallization (DRX) grains. An original JC (O–JC) model and a modified JC (M–JC) model were established. The M–JC model indicated a better prediction than the O–JC model. Still, it was deficient in predicting flow stresses with insufficient coupling effects. Hence, based on the M–JC model, a newly modified JC (NM–JC) model, which further enhances the interaction between strain and strain rate as well as strain and temperature, is proposed. Its projected values can better align with the tested values.  Magnesium alloy is the lowest-density metal structural material with excellent specific strength and stiffness, processing and forming properties, and recyclability ,2. It isA hot processing map was developed by dynamic material modeling . The hotThe constitutive model is a numerical model for characterizing the material’s flow behavior under complex conditions and is usually expressed as a math relation among flow stress and related parameters ,11. FiniThe JC model considers strain hardening, strain rate hardening, and thermal softening. However, it neglects the coupling effect between the three, which makes it less predictive of flow behavior under certain conditions . ConsequIn this work, the uniaxial isothermal hot compression test of a high-strength, heat-resistant Mg–Gd–Y–Zr–Ag alloy was performed. Using the test data, a hot processing map was built. The microstructure during thermal deformation was explored using optical microscopy (OM). By combining the flow curves, heat deformation map, and microstructure, the thermal deformation behavior of this alloy was investigated. To characterize the thermal deformation behavior, the NM–JC model was proposed.The chemical components of the deformed magnesium alloy for the tests are given below .−1) and temperatures of (623–773 K). Cylindrical specimens with dimensions of Φ10 mm × 15 mm were used. In a thermal compression test, the specimen was heated at 5 K/s and maintained for 3 min after attaining a deformation temperature. It was finally compressed to 60% of the original height at a constant strain rate to collect stress-strain data [−1, 0.01 s−1, 0.1 s−1, 1 s−1) and four isothermal points  were designed. A uniaxial isothermal hot compression test was performed on the Gleeble 3180 thermo-mechanical simulator at strain rates of  [To further investigate the flow behavior, a hot processing map was drawn using the dissipative structure theory. It reflects the plastic deformation capacity and the power consumption ratio of energy used to change the microstructure in each process condition. The power consumption rate tion (1) .(1)η=2mCombined with the instability criterion presented in Equation (2) , it can The energy dissipation rate map and instability map are available by calculating the test data with Equations (1) and (2). The hot processing map of the magnesium alloy can be drawn by combining these two maps .−1) in the stable region when the power consumption rate is 31–42%. This is the dynamic recrystallization (DRX) active region. In −1, when In the unstable region, as in −1. −1. −1. As seen from Dislocation climb and cross-slip are limited by magnesium alloys’ lower SFE, which results in a lower dynamic rate of recovery and higher dislocation density, which facilitates the occurrence of dynamic recrystallization ,31. Figu−1 for strain and temperature, respectively. The original JC model (O–JC) is a structurally simple function expressed as a math relation among flow stress and deformation parameters, represented in Equation (3) .(3)σ=(Ation (4) .(4)ln(σAccording to a relationship of tion (5)  at 623 K−1.According to a relationship of tion (6)  at 0.001According to the relationship of The tested stress and estimated stress by the O–JC are presented in −1.With higher strain rates and temperatures, the O–JC model must be improved in predicting flow stresses. Lin et al.  took thetion (9)  at 623 KThe experimental data at the reference condition was subjected to quadratic polynomial fitting to obtain ion (10) .(10)σA1−1.According to the relationship of ion (11)  at 0.001−1, 0.01 s−1, 0.1 s−1, and 1 s−1, the corresponding parameters k are −0.01416, −0.01281, −0.01006, and −0.00636, respectively. The correlation of k and According to the relationship of The M–JC model is shown in the following equation:−1 and 0.01 s−1, overall predictions are best. However, the model’s prediction deteriorates with either decreasing temperature or growing strain rates.The tested stress and estimated stress by M–JC are presented in n is solved similarly to the O–JC model. Parameter n is 0.082. Parameter B is temporarily considered to be the constant 22.291. At 623 K, Equation (13) is transformed into Equation (14).Obviously, the M–JC is still deficient in predicting flow stresses because they do not sufficiently consider the interaction between strain, strain rate, and temperature. Hence, based on the M–JC model, a newly modified JC (NM–JC) model, which further enhances the interaction between strain and strain rate as well as the interaction between strain and temperature, is proposed, represented in Equation (13).k is introduced to solve the parameters −1.The material constants −1, 0.01 s−1, 0.1 s−1, and 1 s−1, the values of the corresponding parameters k are −0.0142, −0.0128, −0.0101, 0.00037, respectively. The correlation of k and According to the relationship of At this time, all parameters except for parameters According to the relationship of −1.The tested stress and estimated stress by the NM–JC model are presented in To evaluate the predictiveness of the constitutive models established in this study. The R, RE, and AARM were utilized.n is the sample size. This study involved 224 stress-strain sample points in calculating the constitutive model. −1 was researched through uniaxial isothermal compression tests. Some specific conclusions are as follows:(1)Strain, strain rate, and temperature directly impact the deformation behavior. The flow stress increases with increased strain rates and decreased temperature, while it first increases and finally remains stable with increased strain;(2)The hot processing map reveals the deformation temperature should be kept at 673–773 K for this alloy when the power consumption rate is 31–42%;(3)During the high temperature deformation process, the main softening mechanism is DDRX. The DRX grains will be larger, and the volume proportion of DRX grains will also increase in response to the temperature rise or strain rate decrease;(4)The NM–JC model more perfectly predicts the thermal deformation behavior of this magnesium alloy compared with the O–JC and M–JC models. The R and AARE of the flow stresses predicted by NM–JC reached 0.980 and 8.4%, respectively.The thermal deformation behavior of Mg–Gd–Y–Zr–Ag alloy at 623–773 K and 0.001–1 s


JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.This case describes a young man with an unusual cause of severe osteoporosis and markedly deranged bone microarchitecture resulting in multiple fractures. A potentially pathogenic germline variant in the runt‐related transcription factor 1 (RUNX1) gene was discovered by a focused 51‐gene myeloid malignancy panel during investigation for his unexplained normochromic normocytic anemia. Further bone‐specific genetic testing and a pedigree analysis were declined by the patient. Recent experimental evidence demonstrates that RUNX1 plays a key role in the regulation of osteogenesis and bone homeostasis during skeletal development, mediated by the bone morphogenic protein and Wnt signaling pathways. Therefore, rarer causes of osteoporosis, including those affecting bone formation, should be considered in young patients with multiple unexpected minimal trauma fractures. © 2023 The Authors.  A young man with a normochromic normocytic anaemia, severe osteoporosis and deranged bone microarchitecture resulting in multiple fractures associated with a potentially pathogenic germline variant in the runt‐related transcription factor 1 (RUNX1) gene. As RUNX1 plays a key role in skeletal development, further clinical investigation in this area is warranted. Although many such cases are due to underlying primary or secondary causes, some patients present without an identifiable cause, termed idiopathic osteoporosis (IOP), which has been associated with abnormal bone microarchitecture. It is thought that this patient cohort may have a yet uncharacterized primary genetic basis, with previous molecular screening of young osteoporotic adults revealing several rare known or novel pathogenic gene variants. However, despite these findings, many patients undergoing genetic screening analysis do not have identifiable mutations in causal genes, suggesting that further research into this area is required. Here we describe osteoporosis with impaired bone microarchitecture in a young man with a novel and potentially pathogenic germline genetic variant in runt‐related transcription factor 1 (RUNX1).Osteoporosis in younger adults  is often challenging to diagnose and treat due to several factors, including a poor understanding of the underlying pathophysiology, lack of unified guidelines for diagnosis and management, and limited research in this area.1 Alth2) and −2.3 at the hip (0.659 g/cm2). A family history for osteoporosis was positive in his mother who had had a minimal trauma ankle fracture in her fifties. He had no glucocorticoid exposure or other chronic illness. A spine MRI at age 39 showed T6 and T7 vertebral compression fractures, without any evidence of skeletal dysplasia on imaging of the spine, pelvis, or hips. He had also undergone mandibular surgery for idiopathic condylysis in the past and had normal facies without body disproportion and no other dental abnormalities, hyper‐ or hypomobility, skin lesions, deafness, or ophthalmological issues. He was a nonsmoker with minimal alcohol intake, with a weight of 58 kg, height 168 cm, and a BMI of 20.5 kg/m2.A 40‐year‐old male presented for review of osteoporosis in the setting of several fractures, including clavicle (high‐impact), at age 16 years, as well as minimal trauma ankle and wrist fractures in his early twenties. Following a vertebral and elbow fracture sustained from a low‐velocity fall at age 39 years, severe osteoporosis was diagnosed on dual energy X‐ray absorptiometry (DXA) with a bone mineral density (BMD) Z‐score of −3.6 at the spine (0.685 g/cm and serum β cross‐linked C telopeptide (CTX) was 388 ng/L (N 100–600 ng/L); the serum 25‐hydroxyvitamin D had increased to 88 nmol/L. Serum‐corrected calcium was 2.26 mmol/L (N 2.15–2.55 mmol/L), phosphate was 1.05 mmol/L (N 0.8–1.5 mmol/L), parathyroid hormone (PTH) was 5.2 pmol/L (N 1.6–6.9 pmol/L), alkaline phosphatase was 50 U/L (N 25–110 U/L), and other liver function tests, as well as thyroid function tests and glucose and kidney function (eGFR >90 ml/min/1.73m2), were all normal, and his testosterone was normal, as was the erythrocyte sedimentation rate (ESR). Serum coeliac disease autoantibodies  were negative, as were serum and urine electrophoresis. Spot urinary calcium and creatinine were 3.1 and 2.9 mmol/L respectively, with a raised calcium/creatinine ratio of 1.07 mmol/mmol (N 0.1–0.5 mmol/mmol). In addition, a radionuclide bone scan (technetium Tc 99 m hydroxydiphosphonate) did not demonstrate any abnormal uptake.An extensive screen for secondary causes of osteoporosis was negative, apart from mild vitamin D deficiency (40 nmol/L), which was promptly corrected. Investigations revealed a normal serum tryptase and full blood count. The serum amino‐terminal propeptide of type 1 collagen (P1NP) bone formation marker was 56 μg/L (N 15–80 μg/L)5 and 2) and −1.9 (+ 0.065 g/cm2) at the hip within 2 years. At age 43, he had increased lethargy and malaise with associated weight loss of 8 kg over several months, with the diagnosis of a new normochromic and normocytic anemia with a Hb of 95 g/L (baseline 130 g/L). General examination was unremarkable; he was normotensive with a standing blood pressure of 114/75 mmHg, and there were no rashes or arthropathies. Further investigations revealed microscopic hematuria with glomerular red blood cells in the urine and an elevated serum creatinine of 117 μmol/L (baseline 78 μmol/L) and mild proteinuria of 280 mg/d (N < 165 mg/d) with an elevated albumin/creatinine ratio (ACR) of 14.0 mg/mmol (N < 2.5 mg/mmol). Anti–neutrophil cytoplasmic antibodies (ANCA), a further serum myeloma screen, and anti–nuclear antibodies (ANA), anti–double–stranded deoxyribonucleic acid (dsDNA), rheumatoid factor (RF), and hepatitis serology were negative, and an abdominal ultrasound was normal.Treatment comprised optimizing dietary calcium and initiating regular progressive resistance training exercise and commencing weekly 35 mg oral risedronate. His BMD improved, with a spinal Z‐score of −2.8  Fig. , and he The patient will have close clinical follow‐up with hematology as he continues to have an isolated normochromic normocytic anemia, as well as ongoing renal and endocrinology review. His response to teriparatide will be monitored with repeat DXA and HRpQCT at 12 months. and skeletal development. A wide range of both germline and somatic RUNX1 genetic variants have been associated with various myeloid and lymphoblastic malignancies. Genetic screening has revealed that RUNX1 is one of the most frequently altered genes in myelodysplastic syndromes (MDS) and, when present in acute myeloid or lymphoblastic leukemia, is associated with an overall poorer prognosis.RUNX1 plays a critical role during organogenesis, including definitive hematopoiesis6 and  is involved in sternal development, regulates endochondral ossification during fracture healing, and upregulates several bone‐specific genes associated with bone formation and homeostasis in trabecular and cortical bone. However, despite the detection of RUNX1 in osteoblast progenitors, preosteoblasts, and mature osteoblasts, the exact mechanisms regulating bone maturation are unknown, and our understanding of the role of RUNX1 in bone formation is evolving.From a bone viewpoint, RUNX1 induces skeletal progenitor cells into early stages of chondrogenesis,13 is  Specifically, it was shown that RUNX1 deficiency impaired both BMP and TGF‐β signaling in the femur, tibia, and calvarial cells of the knockout mice, in addition to impaired β‐catenin signaling with a downregulation of active β‐catenin protein levels in trabecular bone. This led to reduced bone density, bone volume, and trabecular number and an increase in trabecular bone separation, which parallel the HRpQCT findings demonstrated in our patient. In RUNX1 conditional knockout mice, serum P1NP was decreased but CTX levels were unchanged compared with wildtype mice. Interestingly, in an ovariectomized animal model used to simulate estrogen depletion‐induced osteoporosis, RUNX1 overexpression via an adeno‐associated virus (AAV)‐mediated gene expression significantly increased bone volume.Recent experimental evidence utilizing RUNX1 conditional knockout mice has revealed that RUNX1 enhances osteoblast differentiation, thereby promoting bone formation and inhibiting adipogenesis by upregulating the Bmp7/Alk3/Smad1/5/8/Runx2/ATF4 and WNT/β‐catenin signaling pathways, which are involved in bone formation to maintain postnatal and adult bone homeostasis.18 Spe The variant is in a disordered domain C‐terminal to the DNA‐binding domain with a cytogenic location of 21q22.12 and has no well‐described function. It is unclear whether this region is established as a transactivation domain (TAD); it is likely between the DNA‐binding domain and the TAD. This variant (rs771614642) is rare, with an allele frequency of 0.000046 in a large population dataset . Ensemble missense variant prediction analysis suggests a potential deleterious effect on protein structure/function . It is currently classified as a variant of uncertain significance (VUS) with multiple listings in the ClinVar human genetic variant database (Variation ID 239055) and one listing associated with MDS in the catalogue of somatic mutations in cancer . Further genotypic and phenotypic characterization of the patient's variant is unavailable as extended segregation analysis has been declined in the patient's extended family, including in his young children. However, the patient's RUNX1 variant was also observed in a recent clinical study sequencing DNA of 4836 children with newly diagnosed B‐cell acute lymphoblastic leukemia (B‐ALL), with two children harboring the germline RUNX1 Arg250His missense variant. In particular, this variant demonstrated a 19% loss in transcription activator activity compared with the wildtype protein using the luciferase reporter assay in HeLa cells, with similar protein levels as compared to wildtype RUNX1 in HeLa cells, and was therefore not deemed to have a significant effect on RUNX1 gene function, so it was not studied further. It is unknown whether the two children with the variant in this study had any phenotypic characteristics suggesting skeletal bone abnormalities. In this patient, the NGS panel was designed to investigate suspected myeloid malignancies and so did not include all known pathogenic variants related to osteopenia. RUNX1 was assigned as the causative variant as no other variant of significance was found. Further functional studies, such as a DNA‐binding assay or reporter assay, would be helpful in assigning causality more strongly to this RUNX1 variant.Given the emerging role of RUNX1 as a central regulator of osteogenesis, we suggest that the RUNX1 variant identified in this patient with a normochromic normocytic anemia and severe osteoporosis is potentially pathogenic. To our knowledge, this is the first case study to report human skeletal bone abnormalities in the context of a heterozygous germline RUNX1 variant. This single‐nucleotide missense variant is c.749G > A with respect to NCBI Reference Sequence NM_001754.5, replacing arginine with histidine, both of which are basic and polar, at codon 250 of the RUNX1 protein (p.Arg250His). The RUNX1 protein is highly conserved between mouse, rat, and human species, with 100% conservation for the region that encompasses the Arg‐250 amino acid.19 The A previous meta‐analysis of genome‐wide association studies (GWAS) in human individuals revealed numerous gene loci linked with BMD variation and risk of fracture. Among those found to be most significant was runt‐related transcription factor 2 (RUNX2), which is a known osteoblast‐specific transcription factor involved in skeletal bone formation and osteoblast differentiation. Indeed, population studies have shown that RUNX2 genetic variation is associated with differences in BMD and risk of fracture. However, no such evidence exists for RUNX1 polymorphisms exerting human low BMD phenotypes. Given the often ubiquitous and transcriptional nature of the affected genes, extraskeletal manifestations affecting other organ systems have been described. In our patient, it is possible that his hematological and nephrological conditions are related to the known RUNX1 variant. Interestingly, a novel Mendelian randomization study provided evidence that the hematopoietic system may regulate the skeletal system in humans, with red and white blood cell traits observed to have positive and inverse causal effects on BMD, respectively. Given the known association of RUNX1 genetic variants in the pathophysiology of human MDS, it is possible that this patient's bone marrow and aspirate findings represent an early “phenotype” of lower‐risk MDS, which may progress in the future due to disruption of antitumor cellular defense. While the patient does not currently meet the criteria for RUNX1 familial platelet disorder with associated myeloid malignancies (FPDMM) or acute myeloid leukemia (AML), the reported pathogenic variants for these disorders are unlike the variant in this case. Although the exact etiology of primary IgAN in humans remains unknown, this polygenic disease is thought to have a significant autoimmune basis with dysregulated IgA synthesis involving both CD4+ (T‐helper) T cells and B lymphocytes (CD5+ CD19+ B cells). Notably, RUNX1 is involved in multiple crucial roles in various immune cell subsets, including the determination of T cell (CD4+/CD8+) lineage choice and early B‐cell development. Whether this young male's IgAN is related to the detected RUNX1 variant is unclear. Evidently, in this patient, there are marked alterations in bone microarchitecture, indicating a possible defect in cortical and trabecular bone formation, as well as bone quality, despite bone turnover markers being in the normal range. Thus, the precise mechanisms underlying these changes await elucidation.Recent genetic sequencing studies of patients with IOP identified a small subset of patients with either known or likely pathogenic gene variants or variants of undetermined significance, affecting defects in bone formation or resulting in high bone turnover.29 A p Metaphyseal cortical bone is formed via corticalization of trabeculae adjacent to the cortex during growth, with disruption of this process resulting in abnormal cortical bone with increased cortical porosity. As a result of the disorganization of bone components with poor interconnection, load is ineffectively transferred, causing tissue damage and subsequent inflammation triggering a cascade of biological and anatomopathological events leading to bone abnormalities, fragility, and ultimately, fractures. This patient's previous history of condylar resorption may represent yet another clinical manifestation of ineffective load conduction in a site of intense, repetitive, and unpredictable mechanical loading. Such fractures may occur independently of bone density and architecture.The hypothesis that disorganized bone formation contributed to the pathogenesis of bone fragility is supported by the presence of marked cortical porosity in this relatively young patient. Classically, increased porosity is due to a bone‐remodeling imbalance leading to bone loss and structural decay in the cortical compartment with advancing age.39 MetTherefore, in a young patient with multiple minimal trauma fractures, it is imperative to consider rarer causes of osteoporosis, especially when they appear in a constellation with other clinical signs and symptoms. Successful identification of an underlying cause can lead to appropriate preventive care, which may also include surveillance and/or treatment of associated and clinically significant conditions. Impaired bone formation should also be considered, and further investigations to assist a diagnosis may include HRpQCT, NGS, assessment of the quality or extent of disorganization of bone, and bone biopsy. In the context of a condition where the primary problem is osteoblast dysfunction, together with profound reductions in BMD, anabolic agent therapy could also be considered. By analogy with the preclinical studies of RUNX1 overexpression, there may also be a role for RUNX1 as a potential therapeutic target for novel osteoporosis therapy.All serum chemistries were performed at Melbourne Pathology, NATA Accreditation No. 2133, Site No. 2126.Dual energy X‐ray absorptiometry measurements were performed on the same machine at both time points  at I‐MED Radiology Network, Caulfield Hospital, VIC 3162, Australia.Renal biopsy histological analysis performed at Monash Health Pathology, NATA Accreditation No. 2762. Site No. 2755.High‐resolution peripheral quantitative computed tomography images acquired at the distal radius and tibia using Xtreme CTII  as per standard protocol. The scan region of interest is 9.5 mm proximal to the radial midjoint and 22.5 mm proximal to the tibial midjoint.Bone marrow aspiration and trephine biopsy processed at Alfred Health Pathology, NATA Accreditation No. 2349, Site No. 2342. Reported by Hematologist Dr Anna Kalff, who has a special interest in malignant hematology.Next‐generation sequencing custom myeloid panel conducted on genomic DNA and performed at Alfred Health Pathology, NATA Accreditation No. 2349, Site No. 2342. Curation is per ACMG guidelines and reporting by molecular pathologists. The panel has 51 genes that include all of those recommend by an expert curation team from the Australian Leukaemia and Lymphoma Group (ALLG).Exact DNA was extracted from the bone marrow. Libraries were prepared from genomic DNA according to the SureSelect XT HS2 DNA System with Pre‐Capture Pooling manual (Agilent). Quality and quantity of libraries were determined by TapeStation using D1000 ScreenTape (Agilent). Sequencing was performed on an Illumina Miniseq platform (Illumina) on Mid output 300‐cycle cartridge (2 × 150bp).Gene panel targets: All exons in the following genes are covered unless otherwise specified: ASXL1, BCOR, BCORL1, CALR , CBL , CEBPA, CISH, CSF3R , DNMT3A, EPOR, ETV6, EZH2, FLT3 , FOXO3, GATA2, GNAS , IDH1 , IDH2 , JAK1 , JAK2 , JAK3 , KIT , KLF3, KRAS , MPL, NF1, NFE2, NPM1 , NRAS , PHF6, PPM1D, PTPN11 , RAD21, RUNX1, SETBP1 , SF3B1 , SH2B3, SMC1A, SMC3, SOCS1, SOCS2, SOCS3, SRSF2 , STAG1 , STAG2, STAT5B , TET2, TP53, U2AF1 , WT1 , ZRSR2.Bioinformatics analysis and variant reporting: Sample demultiplexing and FASTQ file generation were performed in BaseSpace (Illumina). Alignment of reads to the reference genome (GRCh38) and variant calling was performed using the Dragen Enrichment pipeline (Illumina). VCF files were uploaded via the GenoVic GOS portal into Alissa Interpret for variant curation and reporting. The IGV browser was used to verify called variants and examine the nature and context of mapped reads. Variants were classified into five tiers of pathogenicity based primarily on the ACMG guidelines for germline mutations plus additional in‐house algorithms relevant to somatic variants. Benign and likely benign variants are not reported. Pathogenic, likely pathogenic, and variants of uncertain significance are reported. The variant allele frequency of reported variants is shown as a decimal value .Nothing to declare: Tomasz J. Block, Cat Shore‐Lorenti, Roger Zebaze, Peter G. Kerr, Anna Kalff, Frances Milat. Declared: Andrew Charles Perkins: Served on advisory boards for Novartis Oncology, Abbvie, CTI, and Sierra Oncology. Peter R. Ebeling: Has received research funding and honoraria from Amgen; and research funding from Sanofi, and Alexion.This manuscript received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors.Tomasz J. Block: Conceptualization; data curation; formal analysis; writing – original draft; writing – review and editing. Cat Shore‐Lorenti: Data curation; formal analysis; investigation; writing – review and editing. Roger Zebaze: Writing – review and editing. Peter G. Kerr: Investigation; writing – review and editing. Anna Kalff: Investigation; writing – review and editing. Andrew Charles Perkins: Investigation; writing – review and editing. Peter R. Ebeling: Conceptualization; data curation; formal analysis; investigation; methodology; supervision; writing – review and editing. Frances Milat: Conceptualization; data curation; formal analysis; investigation; methodology; supervision; writing – review and editing.https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/jbm4.10791.The peer review history for this article is available at


Immunotherapy has become a standard‐of‐care for patients with non‐small‐cell lung cancer (NSCLC). Although several biomarkers, such as programmed cell death‐1, have been shown to be useful in selecting patients likely to benefit from immune checkpoint inhibitors (ICIs), more useful and reliable ones should be investigated. The prognostic nutritional index (PNI) is a marker of the immune and nutritional status of the host, and is derived from serum albumin level and peripheral lymphocyte count. Although several groups reported its prognostic role in patients with NSCLC receiving a single ICI, there exist no reports which have demonstrated its role in the first‐line ICI combined with or without chemotherapy.Two‐hundred and eighteen patients with NSCLC were included in the current study and received pembrolizumab alone or chemoimmunotherapy as the first‐line therapy. Cutoff value of the pretreatment PNI was set as 42.17.p =  0.0021) and overall survival  in the entire population, respectively. The multivariate analysis identified the pretreatment PNI as an independent prognosticator for the PFS (p =  0.0011) and OS (p  < 0.0001), and in patients receiving either pembrolizumab alone or chemoimmunotherapy, the pretreatment PNI remained an independent prognostic factor for the OS .Among 218 patients, 123 (56.4%) had a high PNI (≥42.17), while 95 (43.6%) had a low PNI (<42.17). A significant association was observed between the PNI and both the progression‐free survival (PFS; hazard ratio [HR] =  0.67, 95% confidence interval [CI]: 0.51–0.88, The PNI might help clinicians appropriately identifying patients with better treatment outcomes when receiving first‐line ICI therapy. Thirteen patients with an Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 3 or 4 and whose PNI values were unavailable were excluded. Thus, a total of 218 patients were finally included in this retrospective study. The patients intravenously received pembrolizumab monotherapy or platinum doublet chemotherapy plus ICI, including pembrolizumab and atezolizumab, every 3 weeks Table .Ethical approval from the appropriate institutional review boards (IRB) was obtained for this study: Kyusyu University Hospital (IRB No. 2021‐103), Kyusyu Medical Center (IRB No. 21C083), Fukuoka National Hospital (IRB No. F3‐8) and Kyusyu Cancer Center (IRB No. 2021‐21).2.2All clinicopathological information were harvested from medical records. The age, sex, smoking history, body mass index (BMI), ECOG PS, regimen, values of serum albumin, and peripheral lymphocyte count, histology and the PD‐L1 TPS were determined before patients' treatment. A 22C3 pharmDx assay  was used for the immunohistochemical analysis for PD‐L1, and its membranous expression in tumor cells was evaluated by TPS. Periodic radiologic tests, such as computed tomography (CT), magnetic resonance imaging, and positron emission tomography/CT, were used to assess changes in the tumor size, and the response rate was determined according to the Response Evaluation Criteria in Solid Tumors version 1.1.2.33). In consideration of the median follow‐up period (14.4 months), the cutoff value was defined by time‐dependent receiver operating characteristic (ROC) curve analyses at 12 months after the administration of first‐line pembrolizumab alone or combination therapy of ICIs with chemotherapy for the prediction of the overall survival (OS); the value was 42.17 (area under the ROC curve: 0.72).The blood tests were performed within 1 month before the start of first‐line pembrolizumab alone or ICIs with chemotherapy, and the PNI values prior to the treatment were obtained by 10 × serum albumin concentration (g/dL) + 0.005 × peripheral lymphocyte count  was defined as the period from the initiation of the therapy to clinical or radiographic progressive disease (PD), and the OS was defined as the time from the start of the therapy until the day of the last follow‐up or death from any cause. The survival curves constructed by the Kaplan–Meier method were compared between the high‐ and low‐PNI groups using a log‐rank test and hazard ratios (HRs). Cox proportional hazard model were conducted to estimate HR, and performed to evaluate risk factor for the PFS and OS with the backward elimination procedure in which a p value‐based elimination method was employed. The time‐dependent ROC curve to define the cutoff value of PNI was described using the R package (https://cran.r‐project.org/web/packages/timeROC/index.html). Any items that had not been measured, collected or written in the medical record were considered as missing data. In the current study, the missing data were not complemented using the estimated or calculated values. All statistical analyses except for the description of the time‐dependent ROC curve were performed using the JMP® ver. 14.0 and SAS ver. 9.4 (SAS Institute Inc.).Associations between categorical variables, such as patient characteristics, and the PNI or response rate were analyzed using Pearson's 33.1The clinicopathological characteristics of the 218 patients are presented in Table p < 0.0001).The median pretreatment PNI was 44.0 range: 21.9–65.2). The cutoff value was determined as 42.17, according to the time‐dependent ROC curve to predict the OS at 12 months. This value separated the 218 patients into a high‐PNI group of 123 (56.4%) and low‐PNI group of 95 (43.6%) Figure . As show.9–65.2. 3.2p = 0.0021 [Figure p < 0.0001 [Figure The Kaplan–Meier curves of the PFS and OS for the 218 patients receiving pembrolizumab monotherapy or chemoimmunotherapy as the first‐line therapy are shown in Figure 1 Figure  and medi1 Figure ). In add3.3p = 0.0015) and the PNI value (p = 0.0011) significantly affected the PFS with HRs of 3.53  and 1.73 (95% CI: 1.24–2.39) for the worse ECOG PS and a low PNI, respectively. Regarding the OS, ECOG PS (p = 0.0007) and the PNI (p < 0.0001) were independent predictors in the multivariate analysis; the HRs were 3.38 (95% CI: 1.75–5.96) for an ECOG PS of 2 and 2.17 (95% CI: 1.50–3.16) for a low PNI, respectively. We also performed the analysis with the cutoff value of PD‐L1 ≥50%; however, PNI still remained predictive of both PFS and OS (data not shown).We investigated the prognosticators for the PFS and OS in all the patients treated with pembrolizumab monotherapy or chemoimmunotherapy using univariate and multivariate analyses Table . Multiva3.4p = 0.0502 and 0.0171, respectively [Figure p = 0.0182 and 0.0004, respectively [Figure Figure y Figure ). There y Figure ).p = 0.0270; HR = 2.49, p = 0.0006 in the monotherapy and chemoimmunotherapy populations, respectively [Tables p = 0.0190 [Table Finally, we assessed the relationship between the survival and each categorical value by multivariate analyses. A low PNI was shown to be a prognosticator for the OS by the multivariate analysis . 3.5Subsequently, the associations between the response rate and pretreatment PNI were assessed in patients by treatment type. Among the 197 patients whose response data were available, 80 were treated with pembrolizumab monotherapy, while 117 were with chemoimmunotherapy. The former included 42 patients (52.5%) with an ORR and 38 patients (47.5%) with stable disease (SD) or PD. The latter did 70 patients (59.8%) with ORR and 47 patients (40.2%) in SD or PD. Table 4The present study is the first to show that, in NSCLCs treated with first‐line ICI‐based therapy, the pretreatment PNI was an independent prognostic factor; patients with a high PNI had a significantly better PFS and OS than those with a low PNI. In addition, the pretreatment PNI showed potential to predict the OS in both pembrolizumab monotherapy and chemoimmunotherapy patients.Recently, ICIs have been recognized as particularly promising treatment choices because of their additive effects with chemotherapy, even in the absence of PD‐L1 expression.A number of reports have additionally suggested that a host's immunonutritional status may also be a good biomarker which helps predicting the outcomes of ICI‐based treatments. Such an immunonutritional status can be estimated in an inexpensive and simple manner using blood tests. We reported the clinical utility of the PNI in ICI monotherapy beyond first‐line treatment.It is widely acknowledged that the serum albumin level reflects the nutritional status of cancer patients, and it is known negatively correlate with systemic inflammatory response.This study set the cutoff value of the PNI at 42.17 based on the time‐dependent ROC curve at 12 months, given the median observation period (14.4 months). This cutoff value did not result in any specific bias in the patients' background factors, except for the PS, which seemed to be related to the host's nutritional status ; methodology ; writing—original draft . Gouji Toyokawa: Conceptualization ; investigation ; project administration ; writing—review and editing . Sho Wakasu: Data curation (supporting). Fumihiko Kinoshita: Data curation (supporting). Shinkichi Takamori: Data curation (supporting). Kenji Watanabe: Data curation (supporting). Naoki Haratake: Data curation (supporting); writing—review and editing (supporting). Taichi Nagano: Data curation (supporting). Keisuke Kosai: Data curation (supporting). Kazuki Takada: Data curation (supporting). Airi Fujimoto: Data curation (supporting). Kodo Higashijima: Data curation (supporting). Yoshimasa Shiraishi: Data curation (supporting). Kentaro Tanaka: Data curation (supporting). Hiroaki Takeoka: Data curation (supporting). Masaki Okamoto: Data curation (supporting). Takanori Yamashita: Data curation (supporting). Mototsugu Shimokawa: Formal analysis ; methodology . Fumihiro Shoji: Supervision (supporting). Koji Yamazaki: Supervision . Tatsuro Okamoto: Supervision . Takashi Seto: Supervision . Hitoshi Ueda: Supervision . Sadanori Takeo: Supervision . Naoki Nakashima: Supervision . Isamu Okamoto: Supervision . Tomoyoshi Takenaka: Supervision (lead); writing—review and editing (lead). Tomoharu Yoshizumi: Supervision (lead); writing—review and editing (lead).The authors have a potential financial conflict of interest as follows: Fumihiro Shoji has honoraria for lectures from Chugai Pharmaceutical, Eli Lilly Japan, Ono Pharmaceutical and Taiho Pharmaceutical. Tatsuro Okamoto has received research fundings from AnHeart Therapeutics, AstraZeneca, Bristol‐Myers Squibb, Chugai Pharmaceutical, Covidien Japan, Daiichi Sankyo, Eli Lilly Japan, KM Biologics, Merck Biopharma, MSD, Nippon Boehringer Ingelheim, Nippon Kayaku, Novartis Pharma, Pfizer Japan and Taiho Pharmaceutical and has honoraria for lectures from AstraZeneca, Bristol‐Myers Squibb, Chugai Pharmaceutical, Eli Lilly Japan, Johnson & Johnson, MSD, Nippon Boehringer Ingelheim, Nippon Kayaku, Novartis Pharma, Ono Pharmaceutical, Taiho Pharmaceutical and Towa Pharmaceutical. Takashi Seto has received research fundings from Abbvie, Chugai Pharmaceutical, Daiichi Sankyo, Eli Lilly Japan, Kissei Pharmaceutical, MSD, Novartis Pharma, Pfizer Japan and Takeda Pharmaceutical and has honoraria for lectures from AstraZeneca, Bristol‐Myers Squibb, Chugai Pharmaceutical, Covidien Japan, Daiichi Sankyo, Eli Lilly Japan, Kyowa Hakko Kirin, MSD, Mochida Pharmaceutical, Nippon Boehringer Ingelheim, Novartis Pharma, Ono Pharmaceutical, Pfizer Japan, Taiho Pharmaceutical, Takeda Pharmaceutical and Towa Pharmaceutical. Isamu Okamoto has received research grants and personal fees from Chugai Pharma and MSD Oncology, outside the submitted works.Figure S1.Figure S2.Figure S3.Click here for additional data file.Table S1.Table S2.Table S3.Click here for additional data file.


Family physicians' (FPs) long‐term relationships with their oncology patients position them ideally to provide primary palliative care, yet their involvement is variable. We examined perceptions of FP involvement among outpatients receiving palliative care at a cancer center and identified factors associated with this involvement.Patients with advanced cancer attending an oncology palliative care clinic (OPCC) completed a 25‐item survey. Eligible patients had seen an FP within 5 years. Binary multivariable logistic regression analyses were conducted to identify factors associated with (1) having seen an FP for palliative care within 6 months, and (2) having a scheduled/planned FP appointment.p = 0.02), the FP having a 24‐h support service , and a positive perception of FP's care  were associated with having seen the FP for palliative care. English as a first language  and greater ease contacting FP after hours  were positively associated, and female sex of patient  and travel time to FP  negatively associated with having a scheduled/planned FP appointment. Number of OPCC visits was not associated with either outcome.Of 258 patients, 35.2% (89/253) had seen an FP for palliative care within the preceding 6 months, and 51.2% (130/254) had a scheduled/planned FP appointment. Shorter travel time to FP (odds ratio [OR] = 0.67, 95% confidence interval [CI] = 0.48–0.93, Most patients had not seen an FP for palliative care. Accessibility, availability, and equity are important factors to consider when planning interventions to encourage and facilitate access to FPs for palliative care. Enabling factors are conditions, such as transportation and travel time, that facilitate or impede use of services. Need factors include conditions that reflect a requirement for medical treatment, such as the diagnosis of a medical illness. Outcome factors include patient‐reported outcomes, such as symptom control and satisfaction with care; these outcomes are influenced by the predisposing, enabling and need factors and, in turn, affect subsequent healthcare use.Factors that may facilitate or impede FP involvement in palliative care for patients with advanced cancer can be conceptualized using Andersen's Behavioral Model of Health Services Use, which describes four main groups of factors determining access to and use of healthcare services.Although the perspective of FPs on their role in providing cancer palliative care has been well described,22.1Participants were patients with cancer who were attending the Oncology Palliative Care Clinic (OPCC) of the Princess Margaret Cancer Centre, a large integrated cancer treatment, teaching, and research center in Toronto, Canada. The OPCC offers specialized palliative care services to outpatients with cancer, receiving approximately 1500 new referrals annually.2.2Participants' sociodemographic and medical characteristics were extracted from their medical charts using a chart review form developed for the study. A survey was developed to assess patients' perspectives of their FPs' involvement in their care. The survey comprised 25 multi‐component questions with a completion time of approximately 20 minutes. Two categorical survey items formed the two primary study outcomes: (1) having seen the FP for palliative care services  in the last 6 months (versus ≥7 months or never); and (2) having scheduled or planning to schedule an appointment with the FP (versus not having scheduled and not planning to schedule an appointment). Two questions addressed from whom patients currently received and would prefer to receive various cancer and general medical services.ρ = 0.46–0.87) and high internal consistency (Cronbach's α = 0.92), their ratings were summed to create a total score reflecting patients' perception of medical care for use in analyses. An additional item—feeling that it was easy to reach the FP or their team after hours—was included separately to reflect a specific, important aspect of palliative care and was rated on the same 0 to 4 scale.ρ = 0.19–0.39).The remaining questions included items regarding predisposing factors (e.g. patient demographics), enabling factors , and outcome factors . For perceptions of medical care by the FP, seven survey items were rated from 0 (strongly disagree) to 4 (strongly agree): satisfaction with the FP's care; confidence in receiving the best care from the FP; feeling that the FP's role in their care was clear; feeling that the FP provided sufficient time to address their problems; feeling that the FP knew them as a person; receipt of prompt care from their FP; and availability of the FP for desired services. Given the mostly high intercorrelations among these seven items  measure, a validated adaptation of the clinician‐rated ECOG2.3The study received approval from the University Health Network Research Ethics Board (REB #16‐5061‐CE). Participant recruitment in the OPCC was conducted between May 2016 and August 2018. Study staff approached the attending physicians or nurses to confirm eligible patients' physical and cognitive abilities to participate and then approached patients about participating in the study. After receiving information about the study, patients willing to participate provided written informed consent. They either completed the survey immediately or took it home to complete and return by mail using a provided addressed, stamped envelope.2.4The target sample size was 250 participants. Based on our clinical experience with the study population, we estimated that 25% of patients would have seen a FP for palliative care in the last 6 months; thus, with 250 patients, we would estimate that about 63 participants (250 × 25%) would have seen their family physician for palliative care in the last 6 months. Given the minimal number of events per predictor variable (EPV) of ≥10 that would result in valid regression coefficients,t‐tests. Univariable binary logistic regression was used to determine the relationship between positive perception of medical care and each outcome; this was done for the seven perception items individually and for the single score reflecting overall perception of the FP's medical care.Descriptive statistics were used for patients' demographic and medical characteristics and the characteristics of their FP and their care services. Comparisons of groups within each outcome on patients' demographic and medical characteristics were conducted using chi‐square tests, Cochran‐Armitage trend test, and p < 0.25 were entered into the respective stepwise selection procedures; factors with p < 0.05 were retained in the multivariable logistic regression model. Odds ratios (OR) and 95% confidence intervals (CI) for significant factors were reported. Analyses were performed on all available data and conducted using SPSS version 25 and SAS version 9.4. The significance level was set to 0.05.Binary logistic regression analyses were conducted to identify factors associated with the two outcomes. Candidate factors included the following variables from the survey, classified according to Andersen's model33.1A total of 832 patients were screened. Of these, 289 (34.7%) were ineligible . The remaining 543 eligible patients (65.3%) were approached at least once. Of these, 280 (51.6%) declined participation . The remaining 263 (48.4%) consented to the study, and 258 (47.5%) completed the survey.p = 0.02) and more likely to speak English as a first language (p = 0.02) than those who did not have a scheduled or planned appointment.Of those who responded, 89/253  reported having seen their FP for palliative care in the last 6 months, whereas 130/254  had scheduled or planned to schedule an appointment with their FP. Table Table 3.2Table 3.3p = 0.01). The total score, reflecting positive perceptions of overall medical care, was significantly associated with both having visited the FP for palliative care in the last 6 months  and having a scheduled or planned appointment with the FP .Table 3.4p = 0.02), indicating that the FP offers 24‐h telephone support , and better perceived care by the FP .Results of the multivariable analyses are shown in Table p = 0.01), English as first language , travel time to the FP , and ease of reaching the FP after hours .The following variables were negatively or positively associated with having a scheduled appointment with the FP: female sex ; data curation (lead); investigation ; methodology ; writing – original draft ; writing – review and editing . Kenneth Mah: Formal analysis ; writing – original draft ; writing – review and editing . Ashley Pope: Data curation (supporting); project administration (lead); writing – review and editing . Nadia Swami: Investigation ; project administration (supporting); writing – review and editing . Breffni Hannon: Investigation ; writing – review and editing . Jenny Lau: Investigation ; writing – review and editing . Ernie Mak: Investigation ; writing – review and editing . Ahmed Al‐Awamer: Investigation ; writing – review and editing . Subrata Banerjee: Investigation ; writing – review and editing . Laura A. Dawson: Investigation ; methodology ; writing – review and editing . Amna Husain: Investigation ; methodology ; writing – review and editing . Gary Rodin: Investigation ; methodology ; writing – review and editing . Lisa W. Le: Conceptualization ; formal analysis ; methodology ; writing – review and editing . Camilla Zimmermann: Conceptualization ; investigation ; methodology ; writing ‐ original draft ; writing ‐ review and editing .This study was funded by the Canadian Institutes of Health Research (Grant No. 152996 to Dr. Zimmermann). The funding body had no role in the design of the study, collection, analysis, and interpretation of data or in writing the manuscript.We declare that we have no competing conflicts of interest.This study was conducted in line with the principles of the Declaration of Helsinki. Approval was granted by the University Health Network Research Ethics Board (REB #16‐5061‐CE).