HIV cure strategies aim to eliminate viral reservoirs that persist despite successful antiretroviral therapy (ART). We have previously described that 9% of HIV‐infected individuals who receive ART harbor low levels of provirus (LoViReTs).6 CD4+ T cells, respectively. We measured HIV reservoirs in blood and host genetic factors. Fourteen LoViReTs underwent leukapheresis to analyze replication‐competent virus, and HIV‐DNA in CD4+ T‐cell subpopulations. Additionally, we measured HIV‐DNA in rectum and/or lymph node biopsies from nine of them.We selected 22 LoViReTs matched with 22 controls ART suppressed for more than 3 years with fewer than 100 and more than 100 HIV‐DNA copies/10We found that LoViReTs harbored not only lower levels of total HIV‐DNA, but also significantly lower intact HIV‐DNA, cell‐associated HIV‐RNA, and ultrasensitive viral load than controls. The proportion of intact versus total proviruses was similar in both groups. We found no differences in the percentage of host factors. In peripheral blood, 71% of LoViReTs had undetectable replication‐competent virus. Minimum levels of total HIV‐DNA were found in rectal and lymph node biopsies compared with HIV‐infected individuals receiving ART. The main contributors to the reservoir were short‐lived transitional memory and effector memory T cells , indicating an altered distribution of the HIV reservoir in the peripheral T‐cell subpopulations of LoViReTs.In conclusion, LoViReTs are characterized by low levels of viral reservoir in peripheral blood and secondary lymphoid tissues, which might be explained by an altered distribution of the proviral HIV‐DNA towards more short‐lived memory T cells. LoViReTs can be considered exceptional candidates for future interventions aimed at curing HIV.  These proviruses constitute the viral reservoir, which is the main barrier to curing HIV. Thus, most HIV‐infected individuals experience a rapid relapse of viremia when ART is interrupted. However, an extremely small proportion of HIV‐infected individuals control viral replication naturally in the absence of ART and usually harbor low viral reservoirs. These individuals are considered examples of functional cure and comprise phenotypes such as elite controllers ) .Since the latent reservoir may be unevenly represented in lymphoid tissues and peripheral blood, we explored potential secondary lymphoid tissue by collecting rectum and lymph node biopsies in a subset of eight LoViReT individuals with an IUPM <0.1 Fig. . Minimum7]) Fig. . These v6 CD45RA– T cells (36.3–2112])  than for individuals receiving ART in previous studies ) Fig. . In this studies , 26.Matching of detectable/undetectable proviral HIV‐DNA reservoir in rectal biopsies agreed with peripheral blood findings in 75% of individuals. We also observed 57% agreement between rectal and lymph node biopsies.In summary, LoViReT individuals have a lower or even undetectable reservoir compared with people on ART, not only in peripheral blood but also in specific lymphoid tissues such as rectum and lymph nodes.n = 14) and early‐treated LoViReTs . We did not find statistically significant differences between the groups in peripheral blood reservoirs for total HIV‐DNA (p = 0.98), caHIV‐RNA (p = 0.81), usVL (p = 0.53), or replication‐competent proviruses (p = 0.55)  or lymph node specimens (p = 0.23)  Fig. . Similar23) Fig. .+ T‐cell subpopulation. To do so, we sorted five CD4+ T‐cell subpopulations  from 14 LoViReT individuals and quantified total HIV‐DNA in each subpopulation .Finally, we aimed to determine whether the low level of proviruses observed in LoViReT individuals could be attributed to altered HIV persistence in a specific CD4ion Fig. . The res+ T cells , closely followed by TEM and TCM .We then evaluated the contribution of each subset to the HIV reservoir by calculating the frequency of each subpopulation of CD4lls Fig. . Both TN+ T‐cell subpopulations of LoViReT individuals is altered, thus potentially explaining the lower reservoir levels observed.Therefore, it appears that the distribution of the HIV reservoir in the CD45 HIV‐RNA copies/ml, that is similar to those of control individuals [We previously described a new phenotype of HIV‐infected individuals on ART who harbor low levels of HIV‐DNA in peripheral blood, namely, the LoViReT cohort . These iividuals . However+ T cells, but also that these proviruses were less transcriptionally active and produced fewer new virions. This finding took the form of lower detectability of residual plasma viremia. Our observations are in line with data from individuals with low levels of HIV‐DNA, such as elite controllers [Our results confirm not only a low level of HIV reservoirs in LoViReT individuals in CD4trollers , exceptitrollers , in whom+ T cells, we found lower levels of intact proviruses in LoViReT participants. Nevertheless, those differences seemed to be the consequence of the lower levels of total reservoir, since the proportion to total proviruses did not differ between the groups. In addition, we observed a positive correlation between total and intact proviruses. Interestingly, levels of intact proviruses were undetectable in nine individuals in the LoViReT group (56%) and two individuals in the control group (10.5%), the latter being consistent with the 12% reported elsewhere [In addition, when we measured the nature of the HIV reservoir in peripheral CD4lsewhere . Thus, o+ T cells. We observed that 71% of LoViReT individuals did not have replication‐competent proviruses . Recent studies have shown that autologous contemporaneous IgG antibodies, if added to the culture, can block outgrowth of a substantial fraction of viruses in the latent reservoir for HIV‐1 by direct neutralization of the virus [To complement the molecular measurements, we set up a highly sensitive viral outgrowth assay based on ex vivo stimulation of 38 million peripheral CD4he virus . Therefohe virus .Despite the low level of replication‐competent proviruses, recent studies have shown that analytical treatment interruption in individuals with characteristics similar to those of LoViReT predominately led to a viral rebound within 2–12 weeks after stopping ART , 16, 31.+ T‐cell subpopulations. Short‐lived TTM and TEM contributed considerably to the total HIV reservoir in LoViReTs. The rapid turnover rate of these cells [+ T cells with a shorter cellular half‐life are also more easily cleared through successful ART [The factor that might explain the total HIV‐DNA dynamic observed in these individuals is the distribution of the proviruses in CD4se cells  contribuse cells . Howeversful ART , suggestsful ART  and thussful ART .TM and TEM are characterized by higher levels of reactivation upon treatment with some latency‐reversing agents [CM [+ T cells—including TN and TCM—accounted for only 24% of the total HIV reservoir. This limited contribution of TCM is also found in the following scenarios: the classic sooty‐mangabey model of attenuated SIV infection related to low CCR5 coreceptor expression [Furthermore, Tg agents ; this coents [CM , while ipression ; HIV‐2 ipression ; post‐trpression ; and lonpression .Although LoViReT individuals have an interesting phenotype with respect to reduction of the HIV reservoir, this cohort differs from the previously described phenotypes of control. For example, 13% of post‐treatment controllers had been treated in the early phase of the infection while only 4% had been treated in the chronic phase . InteresCM from infection and that this in turn might have helped to attain some degree of control in establishment of the HIV reservoir. Nevertheless, it would be interesting to explore whether integration of the proviruses could be undertaken at sites that are not transcriptionally active, as recently shown for elite controllers [Control of HIV is characterized by the over‐representation of protective HLA‐class I alleles in long‐term nonprogressors , elite ctrollers .Our study has some limitations. First, we did not obtain leukapheresis or secondary lymphoid tissues from control individuals, and therefore those values were compared to historical controls. In addition, given that we have a limited number of LoViReT individuals, we anticipate having a validation cohort, for which screening has already started, and that will contribute to deciphering the mechanisms of reduction of HIV reservoirs. Nevertheless, it is important to highlight that the screening of our current LoViReT cohort was carried out at two independent clinical sites in Barcelona, and a similar proportion of LoViReT individuals were obtained at both .+ T‐cell subpopulations with shorter half‐lives. Their special characteristics might make them potential candidates for HIV cure strategies. These strategies should be aimed at further boosting the immune response in order to effectively control the potential residual virus that could reactivate from a minimal HIV reservoir in these individuals.In conclusion, we showed that LoViReT individuals differ from other HIV control phenotypes. Factors associated with their status are related to impaired establishment of the reservoir in different lymphoid compartments and to altered proviral distribution towards CD4J. M.‐P. received an institutional grant and educational/consultancy fees from Merck Sharp & Dohme. Outside the submitted work, he has received institutional grants and educational/consultancy fees from AbiVax, AstraZeneca, Gilead Sciences, Grifols, Janssen, and ViiV Healthcare. The remaining authors declare that they have no competing interests.Cristina Gálvez, Javier Martinez‐Picado, and Maria Salgado conceived and designed the study; Cristina Gálvez, Maria del Carmen Garcia‐Guerrero, Sílvia Bernal, and Maria Salgado performed the experiments; Cristina Gálvez, Víctor Urrea, and Maria Salgado analyzed the data; Cristina Gálvez, Javier Martinez‐Picado, and Maria Salgado interpreted the results; Judith Dalmau, Susana Benet, Beatriz Mothe, Lucía Bailón, Andrea Martinez, Aroa Nieto, Bonaventura Clotet, Lorna Leal, and Felipe García contributed to the study design, recruitment of participants, sampling, and management of clinical data. Cristina Gálvez, Javier Martinez‐Picado, and Maria Salgado wrote the paper. All the authors read, reviewed, and approved the final version of the paper.Supplementary Fig. S1 Study design flow‐chart. *Clinical factors used for matching of the groups included sex, age at diagnosis, age at sampling, CD4 T cells at sampling, CD8 T cells at sampling, % of CD4 T cells at sampling, % of CD8 T cells at sampling, CD4/CD8 ratio at sampling, number of blips, number of virological failures, zenith viral load, nadir CD4 T cells, time since undetectable viral load (50 HIV‐RNA copies/ml), total time with undetectable viral load, time from diagnosis to undetectable viral load, area under the curve for viral load normalized according to time undetectable, and AIDS events. IUPM = infectious units per million.Supplementary Fig. S2 HLA class I profile and CCR5wt/Δ32 frequency. (a) Frequency of the protective alleles HLA‐B*27 and B*57, risk alleles HLA‐B*07:02 and B*35, and genotype CCR5wt/Δ32 in LoViReT individuals and controls. (b) Frequency of individuals with the CCR5Δ32 mutation in heterozygosis.Click here for additional data file.


First‐generation epidermal growth factor receptor‐tyrosine kinase inhibitor (EGFR‐TKI) sometimes causes lung injury, thereby affecting survival. Although pre‐existing interstitial lung abnormal shadow (pre‐ILS) increases the risk of lung injury by EGFR‐TKIs, its impact on osimertinib, a third‐generation EGFR‐TKI, remains unknown.EGFR‐mutated non‐small cell lung cancer treated with osimertinib. Computed tomography images were obtained and evaluated independently by three pulmonologists in a blinded manner. Factors associated with lung injury were assessed using a logistic regression model. Survival curves were calculated by the Kaplan–Meier method and compared using a log‐rank test.This retrospective cohort study consecutively enrolled patients of p = 0.025). Severe (≥Grade 3) lung injury was observed in eight (4.1%) patients, of whom, two (5%) and six (3.9%) had and did not have pre‐ILS (p = 0.67), respectively. Grade 5 lung injury was not observed, and survival curves were similar between the patients who developed lung injury and those who did not .Of the 195 patients, 40 had pre‐ILS, and 21 (8 with and 13 without pre‐ILS) developed lung injury during the observation period. Multivariate analysis revealed that pre‐ILS was independently associated with lung injury  in the treatment with osimertinib. Notably, the incidence of grade ≥3 was similar between patients with and without pre‐ILS, which was 5% and 3.9%, respectively. The overall survival was similar for patients who developed lung injury and those who did not. However, we still need to be cautious during treatment with osimertinib in patients with pre‐ILS; further studies are warranted in this field.Although pre‐existing interstitial lung abnormal (pre‐ILS) shadow has been known to be a risk factor of lung injury by EGFR‐TKIs, data on osimertinib are lacking. Current results demonstrated that pre‐ILS increases the risk of lung injury (odds ratio, 3.1; 95% confidence interval, 1.1–8.2;  To avoid case selection bias, we collected all the cases consecutively in which osimertinib was prescribed in Wakayama Prefecture between November 2015 (when osimertinib was approved in Japan) and June 2019. Specifically, we contacted all the hospitals that treat lung cancer in Wakayama Prefecture and requested eight institutions with at least one case of osimertinib prescription to participate in this study. We obtained consent from all the institutions. The median observation period was 12 months .2.2Interstitial lung abnormal shadow was defined as the presence of nondependent changes, which included ground‐glass or reticular abnormalities, diffuse centrilobular nodularity, non‐emphysematous cysts, honeycombing, and traction bronchiectasis regardless of the lung field occupying area. The lung area affected by pre‐ILS was classified into three grades based on the chest computed tomography (CT) findings as follows: 0%, 1%–4%, and ≥5%. In line with a position paper from the Fleischner Society in 2020, interstitial lung abnormalities (ILA) were defined as the occupation of more than 5% of the lung field among the ILS, and the others (the occupation of less than 5% of the lung field) was assigned to indeterminant‐ILA (ind‐ILA). Radiation pneumonitis, which was determined according to the history of radiation therapy and imaging features, and shadow by lung cancer were excluded from all image evaluations. Computed tomography images were reconstructed using a 1–5‐mm slice thickness. Three pulmonologists  independently performed a review of the serial CT images. All evaluations were completed without any preliminary knowledge of the patients or other doctors' decisions. Final decisions were made based on a majority, and when the results were equally divided, a consensus was made through discussion. These methods for the chest image evaluation were previously used and reported.2.3The diagnostic criteria for osimertinib‐induced lung injury were as follows: (1) newly emerged lesion during treatment with osimertinib, (2) ground‐glass attenuation or infiltrative shadow on chest CT, (3) no indication of pulmonary infection including purulent sputum, improvement by antibiotic treatment, and positive results for sputum and/or blood cultures. Patients with an abnormal shadow accompanied by a relevant bronchial obstruction, apparent heart failure, or pulmonary invasion of lung cancer were excluded. In cases of lung injury, chest CT images of the lung injury were also evaluated by three pulmonologists as described above. The phenotypical patterns of lung injury were classified as acute eosinophilic pneumonia (AEP), non‐cardiogenic pulmonary edema (NCPE), non‐specific interstitial pneumonia (NSIP), organizing pneumonia (OP), hypersensitivity pneumonia (HP), or diffuse alveolar damage (DAD) according to the consensus statement for the diagnosis and treatment of drug‐induced lung injuries from Japanese Respiratory Society, which was based on an official American Thoracic Society/European Respiratory Society statement for the international multidisciplinary classification of the idiopathic interstitial pneumonias.2.4Overall survival was defined as the interval from the date of initial administration of osimertinib to the date of death from any cause or of the last follow‐up examination. Toxicity was assessed using the Common Terminology Criteria for Adverse Events version 5.0.p < 0.2 in the univariate analysis. All p‐values were based on a two‐sided hypothesis, and p < 0.05 was considered statistically significant. All the statistical analyses were performed using JMP Pro software version 14.1.0 (SAS Institute).Dichotomous variables, such as baseline characteristics, are expressed in numbers and percentages and were compared using Fisher's exact test. Continuous variables are represented as median values and interquartile ranges and were analyzed using the Wilcoxon rank‐sum test. The curves of the cumulative incidence of osimertinib‐induced lung injury were constructed using the Kaplan–Meier method. Multivariate analysis was performed for the factors with 33.1EGFR‐mutated NSCLC treated with osimertinib. Baseline characteristics are shown in Table EGFR mutations were distributed as follows: exon 19 deletion in 41 patients (21%), L858R in 37 patients (19%), major mutation plus T790M in 113 patients (58%), and other mutations in four patients (2.1%). Finally, 115 patients (59%) had received previous EGFR‐TKI treatment, and one patient had a history of immune‐checkpoint inhibitor (ICI) treatment.This study included 195 patients with advanced or recurrent p = 0.0043) and have a higher rate of prior thoracic radiotherapy  than those without pre‐ILS. The remaining observed factors were similar in both groups.Based on the central review of chest CT images immediately before administration of osimertinib, 40 patients (21%) were determined to have pre‐ILS, of which 21 cases were classified as pre‐ILA and 19 as pre‐ind‐ILA. A honeycomb pattern was detected in only one patient. Patients with pre‐ILS tended to be older   .Twenty‐one patients (11%) developed lung injury during the observation period. The incidence of lung injury was significantly higher among patients with pre‐ILS than among those without pre‐ILS  Figure . Moreove) Figure . To expl3.3p = 0.67), and none of the patients developed Grade 5 lung injury , grade 2 in 11 patients (5.6%), and grade 3 in eight patients (4.1%). The incidence of severe lung injury (Common Terminology Criteria for Adverse Events Grade ≥3) was 5% and 3.9% in patients with or without pre‐ILS, respectively , followed by HP and NSIP in four patients each (2.1%), and AEP and NCPE in three patients each (1.5%). Diffuse alveolar damage pattern was not observed. The distribution of the radiographic patterns of lung injury was similar between patients with and without pre‐ILS Figure  and in p3.4p = 0.014).During the observation period, the median interval from the initiation of osimertinib treatment to the onset of pneumonitis was 2.2 months  and 2.4 months  in patients with and without pre‐ILS, respectively. The incidence of pneumonitis at 1 month was 16%  in patients with pre‐ILS and 6.0%  in patients without pre‐ILS; at 3 months, which increased to 19%  and 8.2% , respectively . Patient characteristics did not significantly differ between the two groups  and osimertinib‐induced lung injury centrally evaluated on chest CT images of patients treated with osimertinib. First, pre‐ILS was identified as an independent risk factor for the development of osimertinib‐induced lung injury. Second, the severity of lung injury was similar between the patients with and without pre‐ILS.A recent report summarizing a post‐marketing survey demonstrated that the presence of pre‐ILS and a history of nivolumab treatment are risk factors for the development of osimertinib‐induced lung injury.The present results are the first to demonstrate that the presence of pre‐ILS is a risk factor for osimertinib‐induced lung injury. This study's definition of ILS includes both ILA and ind‐ILA. Since ind‐ILA accounted for 48% of pre‐ILS, it is likely that the cases with relatively minor shadows were accounted for. Notably, the current results also suggest that the extent of pre‐ILS is not critical for the risk of lung injury by osimertinib.Furthermore, we also demonstrated that the presence of pre‐ILS does not affect the severity of osimertinib‐induced lung injury. In patients with pre‐ILS, the severity of lung injury caused by ICIs or chemotherapy is often high. In our previous report of lung injury caused by docetaxel or S‐1 in patients with pre‐ILS, 14 of 18 lung injuries (78%) were Grade 3 or higher.In gefitinib treatment, with approximately 59.9% of severe cases and 35.7%–44.3% of fatal cases, 15–24% of the gefitinib‐induced lung injuries demonstrated DAD patterns in the chest CT images.The mechanisms underlying drug‐induced lung injury are thought to involve direct drug‐induced damage to type II alveolar epithelial cells and activation of immune cells. Gefitinib has been reported to increase the vulnerability of epithelial cells caused by suppression of Hsp70 expression, suggesting that EGFR‐TKIs may damage epithelial cells by their action on normal alveolar epithelium.This study had certain limitations. First, we evaluated patients based on a retrospective review of the medical records from multiple hospitals. Inevitably, there are inter‐hospital differences in CT slice thickness, CT interval, and patient selection for osimertinib treatment. In addition, the diagnosis and treatment of lung injury were based on the criteria that excluded pulmonary infection and heart failure; however, the final decision of diagnosis was made by physicians, allowing for potential bias. Moreover, there was only one case of a honeycomb pattern in this study. This indicates that cases with obvious pre‐ILS may have been excluded from the study because previous reports showed that EGFR‐TKI‐induced lung injury is more common in patients with pre‐ILS. Therefore, the results of the current study, which showed that the severity of lung injury and imaging patterns did not change with the presence of pre‐ILS, should not be immediately applied to all cases of pre‐ILS, especially in patients with honeycomb patterns. Caution should be exercised in these cases until further investigation is performed.Nevertheless, this study is one of the largest investigations focusing on lung injury in patients treated with osimertinib, and all pre‐treatment chest CT images were independently evaluated in a blinded manner. Consequently, the overall bias was relatively low since all the patients treated with osimertinib in Wakayama Prefecture were included.4.1The presence of pre‐ILS increases the risk of lung injury in patients of NSCLC treated with osimertinib. Although the severity of lung injury was not clearly affected by pre‐ILS and the impact of osimertinib‐induced lung injury on survival could be weak, we still need to be cautious during treatment with osimertinib in patients with pre‐ILS.Dr. Ozawa reported receiving honoraria from AstraZeneca K.K. and Chugai Pharmaceutical Co. Ltd. and participation on an advisory board for AstraZeneca K.K. Dr. Teraoka reported receiving consulting fees from Pfizer Inc. and honoraria from AstraZeneca K.K., Boehringer Ingelheim Japan Inc., Chugai Pharmaceutical Co. Ltd., Eli Lilly Japan K.K., Novartis Pharma K.K., Ono Pharmaceutical Co. Ltd., and Taiho Pharmaceutical Co. Ltd. Dr. Akamatsu reported receiving honoraria from AstraZeneca K.K., Boehringer Ingelheim Japan Inc., Bristol‐Myers Squibb, Chugai Pharmaceutical Co. Ltd., Eli Lilly Japan K.K., MSD K.K., Novartis Pharma K.K., Ono Pharmaceutical Co. Ltd., and Taiho Pharmaceutical Co. Ltd. and research funding from Chugai Pharmaceutical Co. Ltd. and MSD K.K. The remaining authors declare no conflict of interest.Ryota Shibaki: Data curation; methodology; formal analysis; investigation; resources; visualization; project administration; writing – original draft; and writing – review and editing. Yuichi Ozawa: Investigation; validation; writing – original draft; and writing – review and editing. Susumu Noguchi: Investigation; resources; and writing – review and editing. Yusuke Murakami: Investigation; resources; and writing – review and editing. Eri Takase: Investigation; resources; and writing – review and editing. Yuichiro Azuma: Investigation; Resources; and writing – review and editing. Masaru Maebeya: Investigation; resources; and writing – review and editing. Takeya Sugimoto: Investigation; resources; and writing – review and editing. Atsushi Hayata: Investigation; validation; resources; and writing – review and editing. Takahiro Hayakawa: Investigation; resources; and writing – review and editing. Shinya Tamaki: Investigation; resources; and writing – review and editing. Masanori Nakanishi: Investigation; validation; resources; and writing – review and editing. Shunsuke Teraoka: Investigation; resources; and writing – review and editing. Hiroaki Akamatsu: Investigation; resources; and writing – review and editing.The study design was approved by the appropriate ethics review board of the Wakayama Medical University, Wakayama, Japan (No. 2172). This study was conducted in accordance with the principles of the Declaration of Helsinki. We did not obtain formal consent from the patients because of the nature of the retrospective observational study. Instead, we provided all patients with the opportunity to opt out.Figure S1Click here for additional data file.Table S1Click here for additional data file.


Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research.More than 70% of women sustaining fractures have osteopenia or “normal” bone mineral density (BMD). These women remain undetected using the BMD threshold of −2.5 SD for osteoporosis. As microstructural deterioration increases bone fragility disproportionate to the bone loss producing osteopenia/normal BMD, we hypothesized that the structural fragility score (SFS) of ≥70 units, a measure capturing severe cortical and trabecular deterioration, will identify these women. Distal radial images were acquired using high‐resolution peripheral quantitative tomography in postmenopausal French women, mean age 67 years (range 42–96 years); 1539 women were followed for 4 years (QUALYOR) and 561 women followed for 8 years (OFELY). Women with osteopenia or normal BMD accounted for ~80% of fractures. Women ≥70 years, 29.2% of the cohort, accounted for 39.2% to 61.5% of fractures depending on follow‐up duration. Women having fractures had a higher SFS, lower BMD, and a higher fracture risk assessment score (FRAX) than women remaining fracture‐free. In each BMD category , fracture incidence was two to three times higher in women with SFS ≥70 than <70. In multivariable analyses, associations with fractures remained for BMD and SFS, not FRAX. BMD was no longer, or weakly, associated with fractures after accounting for SFS, whereas SFS remained associated with fracture after accounting for BMD. SFS detected two‐to threefold more women having fractures than BMD or FRAX. SFS in women with osteopenia/normal BMD conferred an odds ratio for fracture of 2.69 to 5.19 for women of any age and 4.98 to 12.2 for women ≥70 years. Receiver‐operator curve (ROC) analyses showed a significant area under the curve (AUC) for SFS, but not BMD or FRAX for the women ≥70 years of age. Targeting women aged ≥70 years with osteopenia indicated that treating 25% using SFS to allocate treatment conferred a cost‐effectiveness ratio < USD $21,000/QALY saved. Quantifying microstructural deterioration complements BMD by identifying women without osteoporosis at imminent and longer‐term fracture risk. © 2019 The Authors.  T‐score of −2.5 standardized deviations (SD) or lower.The morbidity, mortality, and cost of fragility fractures is increasing, in part, because longevity increases the proportion of the population over 65 years of age.T‐score between −2.5 and −1.0 SD) or so‐called “normal” BMD (T‐score above −1 SD), not the smaller numbers of women in the community with severe deficits in BMD designated as “osteoporosis.”Just as the morbidity, mortality, and cost of cerebrovascular disease arises among persons with moderate hypertension,Curbing the population burden of fractures requires a means of identifying women with bone fragility erroneously perceived to be at low risk because they have osteopenia or normal BMD. A rational approach to meet this challenge is to measure bone microstructural deterioration because deterioration in cortical and trabecular architecture increases bone fragility exponentially, disproportionate to the bone loss producing it and the modest deficits in BMD found in postmenopausal women with osteopenia or so‐called “normal” BMD.Cross‐sectional studies demonstrate that a measurement of microstructural deterioration distinguishes women with osteopenia and prevalent fractures from women with osteopenia without fractures.We developed and validated a surrogate of bone fragility relatively free of microstructural determinants of bone strength assembled during growth.T‐scores at the hip or spine between −1.0 and −2.5 SD with clinical risk factors for fracture or −3.0 SD without clinical risk factor.p = 0.37) or with osteopenia or normal BMD (93.3% in OFELY and 92.2% in QUALYOR). Missing values for BMD, FRAX, or SFS resulted in exclusion of 28 women, leaving 2100 women. Consent was obtained from all participants. These studies were approved by the CPP Sud‐Est II institutional review boards, Lyon, France.We studied two population‐based cohorts. The OFELY cohort (Os des Femmes de Lyon) is a prospective study of 1039 women started in February 1992.T‐scores were calculated using NHANES III. Distal radial images were acquired using HRpQCT .Vertebral and nonvertebral fractures were confirmed using radiographs, the dual x‐ray absorptiometry vertebral fracture assessment or reports. Fractures of the head, toes, and fingers were excluded. Femoral neck BMD was measured using Hologic Discovery A in QUALYOR and QDR 4500 in OFELY. x, y) values are projected onto the regression line to quantify the absolute and relative deterioration in these two traits. For women with  coordinates on the regression line, distance B is the absolute deterioration in cortical and trabecular bone. The further the  coordinates are from O along the regression line, the greater the absolute deterioration in both traits and the greater the distance B. The perpendicular distance A captures the differing relative deficits. Women with coordinates above the regression line have relatively more severe cortical than trabecular deterioration. Women with  values below the regression line have the opposite. The SFS = B – A. For women with  coordinates on the regression line, A is zero. When distance A is large, the greater the likelihood that there is a deficit in only one trait, suggesting the deficit is the result of an error in positioning the region of interest, not bone loss. The precision of the SFS  in 15 women having three measurements was 1.12% expressed as the root mean square of the coefficient of variation.We hypothesized that a surrogate measure of bone fragility will capture both the absolute and relative deterioration in cortical and trabecular bone produced by age‐ and menopause‐related bone loss. Demonstration of deterioration in both cortical and trabecular bone is needed because bone loss affects both traits. A deficit in only one trait is likely to reflect errors in positioning of the region of interest, not microstructural deterioration.T‐score ≤ − 2.5 SD and FRAX (with BMD) score > 20. The SFS threshold of 70 was derived by the Youden method, which maximizes the sum of the sensitivity and specificity.t tests were used to compare fracture and nonfracture groups. For non‐normally distributed data, median and interquartile range (IQR) were used as summary statistics and the nonparametric Mann–Whitney test was used to compare groups. For binary variables, summary statistics are presented by cell frequency and percentage. Exact logistic regression was used to compute odds ratios (OR) and their confidence intervals (CI) for associations between each binary predictor  and fracture outcome for each cohort. Testing for equality in ORs between two cohorts was conducted using Mantel–Haenszel test, for all data and for subgroups of women of any age with osteopenia or normal BMD, osteoporosis, and women ≥70 years with osteopenia or normal BMD.De‐identified data were analyzed at the University of Melbourne. The thresholds used were a BMD t test for proportions, and Bonferroni correction method was used to adjust for multiple testing. For the subgroups, exact logistic regression was used to study association between fracture, FRAX, and SFS. Sensitivity and specificity were also given for these two predictors in the subgroup analysis. The performance of each predictor was assessed using the area under the curve (AUC) for all data and subgroups.In the presence of equality in ORs between two cohorts, analyses were conducted for the pooled cohorts using exact logistic regression for univariate analysis and the penalized maximum likelihood logistic regression for multivariable analysis. Pairwise comparisons between proportion of fracture captured by BMD, FRAX, and SFS were carried out using two‐sample http://www.stata.com). A p < 0.05 (two‐tailed) denoted statistical significance.We also analyzed total vBMD, trabecular density, and cortical porosity for women of any age and women aged ≥70 years. The threshold of 231 mgHA/cc was chosen for total vBMD based on the SFS value of 70 units from the fitted regression of SFS on total vBMD. For trabecular density and cortical porosity, the nominal thresholds were the 5th centile (4.8 mgHA/cc) and 90th centile (42.2%), respectively, in premenopausal women. Exact logistic regression or penalized maximum likelihood logistic regression were used to examine associations of these predictors with fractures. All analyses were conducted using STATA , version 15.0 , and they had a higher SFS , FRAX score  but higher BMD T‐score . However, there were no differences in the proportions of women with osteoporosis in the respective cohorts  or with osteopenia or normal BMD . Table At baseline, the women in the OFELY cohort were older than women in the QUALYOR cohort .Figure p = 0.029) and in women osteopenia/normal BMD . However, BMD ≤−2.5 SD was only associated with fracture in women with SFS ≥70  , not low SFS < 70 . The reduction in OR after adjustment was 31.6% for BMD and 13.3% for SFS. The results were similar at 4 years' follow‐up; SFS ≥70 was associated with fracture in women with osteoporosis  and osteopenia/normal BMD . BMD ≤ −2.5 SD was only associated with fracture in women with SFS ≥70 , not SFS < 70 . The reduction in OR after adjustment was 25.6% for BMD and 10% for SFS.Likewise, in a stratification analysis, during 2 years' follow‐up, SFS ≥70 was associated with fractures in women with osteoporosis . SFS was associated with fractures of any type and major fragility fractures at 2, 4, and 8 years in women with osteopenia/normal BMD conferring odds between 2.69 to 5.19 for women of any age and 4.98 to 12.2 for women ≥70 years, and corresponding sensitivities and specificities as shown in Table n = 114 but only 38 followed for 8 years). Figure Table p ranging 0.007 to 0.028). The correlation between SFS and vBMD was −0.83. The threshold of total vBMD corresponding to SFS 70 was 231 mgHA/cc. For women of any age (p < 0.0001).As shown in  any age  and wome any age , reducedThe modeled economic evaluation indicated that if 25% of the screened population were targeted for treatment using the SFS, this approach was likely to be cost‐effective compared with standard care, with an ICER of USD $19,000 per QALY saved and USD $57,000 per year of life saved over a 5‐year time horizon. Over a 10‐year time horizon, the ICERs were USD $4000 per QALY saved and USD $8000 per year of life saved. Cost‐effectiveness also improved if SFS was to be targeted at a population at higher underlying risk of fracture. If 30% of the screened population were targeted for treatment, the ICER would reduce to USD $15,000 per QALY saved over a 5‐year time horizon and USD $47,000 per year of life saved.This study provides data supporting the hypothesis that including a measurement of microstructural deterioration complements the use of BMD by identifying women without osteoporosis at imminent, intermediate, and long‐term risk for fragility fracture. Including a measurement of microstructural deterioration detected ~40% to 60% of women of any age and ~60% to 80% of women ≥70 years of age having any type or major fragility fractures, several‐fold more women than detected using BMD or FRAX during 2, 4, and 8 years' follow‐up. Women with osteopenia or normal BMD would otherwise remain undetected using the diagnostic BMD threshold of –2.5 SD alone.We report that the population burden of fragility fractures arises from two sources. Women with osteopenia or normal BMD accounted for ~80% of all women at imminent 2‐year), intermediate (4‐year), and longer‐term (8‐year) risk for a fragility fracture, not women with osteoporosis. This has been reported in several cross‐sectional studies‐year, inT‐score threshold of –2.5 SD for “osteoporosis” identified none of these women and so only targeting treatment to women with osteoporosis defined as BMD ≤−2.5 SD is unlikely to address the public health burden of fractures. Nor was the FRAX threshold of 20%, a measure of 10‐year risk, sensitive.Distinguishing postmenopausal women with osteopenia or normal BMD having fragility fractures from the majority remaining fracture‐free is a formidable challenge because they comprise only a small percentage of all women with osteopenia or normal BMD in the community. By definition, the BMD The challenge was met, in part, by measuring microstructural deterioration. The BMD measurement does not capture microstructural deterioration. This is a limitation because microstructual deterioration increases fragility disproportionate to the bone loss, causing it and the modest BMD deficits found in postmenopausal women with osteopenia or so‐called “normal” BMD.Within each BMD category , women with cortical and trabecular microstructural deterioration captured by the SFS ≥70 had a two‐ to threefold higher fracture incidence than women with a SFS < 70. From Table The desired property of a surrogate of bone fragility is that it captures the microstructural basis of that fragility. Cortical “porosity” is widely regarded as being the result of bone loss. However, absolute values of cortical porosity (and trabecular density) are the net result of their growth‐dependent assembly, which confers bone strength, and age‐ and menopause‐related bone loss, which confers bone fragility. More than 80% of “pores” are cross sections of Haversian canals in the center of osteons formed during growth.The SFS was also designed to capture concurrent cortical and trabecular deterioration. The presence of coexisting deficits makes it likely that these deficits are the result of bone loss because bone loss is global. A deficit in only one compartment is likely to reflect positioning of the region of interest (ROI).In a cross‐sectional study,Bone loss reduces the amount of bone (captured by BMD) and deteriorates the microstructure of the reduced bone mass (captured by SFS). Both BMD and SFS are associated with fracture; the risk increases as BMD decreases and as SFS increases. Although BMD and SFS were both associated with fracture in univariate analyses, when BMD and SFS were adjusted for each other and for FRAX in multivariable analyses, the ORs for BMD decreased Fig. . In a stFinding similar ORs conferred by BMD and SFS but a greater reduction from unadjusted to adjusted ORs for BMD than for SFS is consistent with confounding. The association between alcohol use and lung cancer decreases after accounting for smoking, but the association between smoking and lung cancer remains after accounting for alcohol use. It is the smoking that confers the risk of cancer. We propose that although bone loss reduces bone mass, it is the microstructural deterioration of that reduced amount of bone that confers the risk of fracture because increased cortical porosity and decreased trabecular density reduce bone strength disproportionate to the bone loss producing this deterioration whether BMD is in the osteoporosis, osteopenic, or normal range Fig. .SFS was a more sensitive predictor of fracture than total vBMD, particularly in women ≥70 years . A measuBone densitometry has been used to estimate fracture risk for more than 50 years and led to the use of three diagnostic categories: “osteoporosis,” “osteopenia,” and “normal” BMD.First, although fracture risk increases as BMD decreases, there is unintended dichotomization of fracture risk. Treatment decisions are often mistakenly made as if bone fragility is present when BMD is ≤−2.5 SD and absent when BMD is >−2.5 SD. This is reflected in the frequent interchangeable description of fractures as “osteoporotic” or “fragility” fractures.T‐score ≤ −2.5 SD, is no assurance that fracture will occur. Indeed, only 14.9% and 22.8% of women with osteoporosis had a fracture of any type during 2 and 4 years' follow‐up and only 12.3% and 19.3% had a major fragility fracture during these respective follow‐up times. It was only after 8 years that a substantial number (36.8%) of these women had a fracture of any type and 26.3% had a major fragility fracture. As in women with osteopenia/normal BMD, the SFS detected 76.5%, 76.9%, and 92.9% of the women with osteoporosis having a fracture of any type during 2, 4, and 8 years, respectively, and 71.4%, 72.3%, and 100% of women having a major fragility fracture during these follow‐up times. These findings suggest that the SFS could also be used to identify women with osteoporosis at imminent risk of fracture within 1 to 2 years needing prompt therapy, perhaps initially using an anabolic agent given there is now evidence of superior efficacy over antiresorptives.Second, finding osteoporosis, a BMD T‐score > −1.0 SD) in postmenopausal women is a misnomer because it gives the impression that bone strength is normal. Postmenopausal women have lost bone and have microstructural deterioration. At 2 years, of the 97 women having a fracture, 12 had “normal” BMD, similar to the number of women with osteoporosis (n = 17). The corresponding numbers of women with normal BMD versus osteoporosis having any type of fracture were 19 and 26 (during 4 years) and 25 and 14 (during 8 years). Of women with “normal” BMD, the SFS ≥70 detected 3/12 (25%) at 2 years, 5/19 (26.3%) at 4 years, and 4/25 (16%) at 8 years. Thus, ironically, the diagnostic threshold of –2.5 SD may result in the failure to treat the very women contributing most of the burden of fractures.Third, as mentioned throughout this article, the term so‐called “normal” BMD (p = 0.38) and SFS and major fragility fracture . When sample sizes were adequate at 8 years, as in the 347 women with osteopenia, the SFS was predictive  with significant AUCs of ~ 0.60 (r = 0.98), albeit at high radiation exposure.This work has several limitations. There were only 38 women with osteoporosis followed for 8 years. This might account for the lack of association between SFS and incidence of any type of fracture , Women of Any Age With Osteopenia/Normal BMD (p2) or Osteoporosis (p3) and Women ≥70 Years of Age With Osteopenia/Normal BMD (p4)Supplemental Table S4. Association Between Fracture and Composite Score of BMD and SFS for Women of Any AgeSupplemental Table S5. Sample Size and Percentage of Fracture and Nonfracture Capture by FRAX, BMD, and SFS for Women of Any AgeSupplemental Table S6. Proportion of Fracture Captured by FRAX, BMD, and SFS for Women of Any Age and Women Aged 70 Years and OlderSupplemental Table S7. Sample Size and Percentage by Fracture Status for (A) the Women of Any Age With Osteopenia or Normal BMD, (B) Women of Any Age With Osteoporosis, and (C) Women ≥70 Years of Age With Osteopenia or Normal BMD, Having Any Fracture or a Major Fragility Fracture During 2, 4, and 8 YearsSupplemental Table S8. ROC Analysis of Any Type of FractureSupplemental Table S9. ROC Analysis of Major Fragility FracturesSupplemental Table S10. Association Between Incident Fractures and Total Volumetric Bone Mineral Density (vBMD) and Structural Fragility Score (SFS) in Women of Any Age and Women ≥70 Years Showing the Odds Ratio (OR) and 95% Confidence Intervals (CI), p Value, Sensitivity, and SpecificitySupplemental Table S11. Association Between Fracture and Binary Predictors Trabecular Density, Cortical Porosity, and the Structural Fragility Score (SFS) for Women of any AgeSupplemental Table S12. Association Between Fracture and Binary Predictors Trabecular Density, Cortical Porosity, and the Structural Fragility Score (SFS) for Women ≥70 YearsClick here for additional data file.


The present study aimed to investigate the association between lipoprotein lipase (LPL) S447X polymorphism and type 2 diabetes mellitus (T2DM), obesity, lipid profile, and oxidative stress parameters in a population from the Kurdistan region of Iraq.We studied 250 adults  aged 45–65 years in four groups, obese and normal body mass index (BMI) diabetic patients versus healthy normal BMI and obese individuals as controls. Lipid profile and oxidative stress parameters were analyzed by colorimetric assay. The LPL S447X genotypes were detected by polymerase chain reaction (PCR)‐restriction fragment length polymorphism.p = 0.03). Also, the presence of this genotype reduced the risk of obesity by 39% . In all individuals, the presence of the SX genotype was associated with significantly lower levels of fasting blood sugar (FBS) and TOS.We found that the obese diabetic group had higher levels of triglycerides (TG), cholesterol, and low‐density lipoprotein‐cholesterol, and lower level of high‐density lipoprotein‐cholesterol than other groups. Obese diabetic patients had higher anthropometric indices than nonobese diabetic patients, obese and normal BMI controls. The levels of TG and total oxidative status (TOS) were significantly lower and higher, respectively, in normal BMI controls than in obese controls. Obese diabetic patients had a lower level of total antioxidant capacity than nondiabetic obese controls. The level of TOS was lower in nondiabetic controls compared to the patient groups. Obese diabetic patients had the highest TOS and malondialdehyde levels. The LPL SX genotype was associated with decreased the risk of T2DM by 79% (odds ratio [OR] = 0.21; 95% confidence interval [CI]: 0.05–0.81, We report the influence of obesity on lipid profile in diabetic and nondiabetic individuals and the effect of LPL SX genotype on decreased risk of T2DM and reduced levels of FBS and TOS. Type 2 diabetes mellitus (T2DM) along with T1DM are the leading causes of the burden of diabetes worldwide.C hemoglobin for predicting CVD in DM patients, even in those with a higher level of blood glucose.Dyslipidemia takes the main role in distributed vascular lesions, is more prevalent in diabetic patients, and places them at a remarkably higher risk for cardiovascular disease (CVD).447→Stop) produces the LPL protein with C‐terminal end truncation by two amino acids.Lipoprotein lipase (LPL) is a glycoprotein enzyme located on chromosome 8.Since the exact mechanism of lipid metabolism dysfunction in diabetes is not fully understood and also the effect of ethnicity on the identification of genes study should not be ignored, the current study was performed to examine the possible association of LPL S447X variants with T2DM and obesity. Also, the effect of LPL S447X genotypes on the levels of oxidative stress, anthropometric and lipid profile parameters were investigated in a population from the Kurdistan of Iraq.22.1n = 69; 27.3%), normal BMI diabetic patients , obese nondiabetic , and normal BMI nondiabetic patients . Nondiabetic individuals were considered as controls.The present case‐control study was carried out in Sulaimani, the Kurdistan region of Iraq. Data was obtained from the medical and paraclinical information of 250 adults from Sulaimani in the Kurdistan region of Iraq. All the participants were recruited from those who referred to the Sulaimani Endocrine and Diabetic Center, public health laboratory, and Sulaimani Shar Hospital from June to December 2021. The study included 129 (51%) females and 121 (49%) males aged 45–65 years. The study population was divided into four groups: obese diabetic patients (2.2T2DM was recognized based on the fasting blood glucose (FBS) over 126 mg/dl, and an oral glucose tolerance test  twice according to the World Health Organization (WHO) guidelines.2.3Individuals aged 45–65 years with T2DM and nondiabetic people with and without obesity were included in the investigation. People with underlying disorders that may affect the study results, such as T1DM, thyroid, and renal dysfunction, CVDs, and malignancy, and people who received medication for dyslipidemia and also pregnant women, were excluded from the investigation.2. BMI was calculated by dividing weight (kg) by the square of height (m2). Also, all the anthropometric indices such as waist circumference, hip circumference, and wrist circumference were measured by a 0.1 cm accuracy stadiometer.Obesity was defined as the body mass index (BMI) above 30 kg/m2.42+ to Cu+ by antioxidants at the wavelength of 450 nm, which was detected via a spectrophotometer. Total oxidative status (TOS) was also measured by a colorimetric method kit (Kiazist Co.). In this method, the ferrous ion (Fe2+) in the presence of oxidants is converted to the ferric ion (Fe3+) that, in the presence of the chromogen, produces a colored solution with an absorbance at the wavelength of 550–580 nm. The malondialdehyde (MDA) concentration was determined colorimetrically using thio‐barbituric acid method.Blood samples were drawn after an overnight fasting. A total of 5 ml of venous blood was obtained from each participant. For DNA extraction, falcon tubes containing ethylenediaminetetraacetic acid were used to store whole blood samples (a volume of 2 ml). In the remaining samples serum was separated for biochemical analysis. FBS was detected using the standard enzymatic method by an automated chemical analyzer (Cobas c311). Lipid profile, including total cholesterol (Chol), TG, high‐density lipoprotein‐cholesterol (HDL‐C), and low‐density lipoprotein‐cholesterol (LDL‐C), were measured with an enzymatic colorimetric assay. Total antioxidant capacity (TAC) was analyzed by a colorimetric assay kit (Kiazist Co.). In this method a color solution is produced by a chromogen reagent affected by the reduction of Cu2.5The phenol‐chloroform method was used for DNA extraction.2.6p < 0.05. In Table χ2 test analyzed the difference between the groups based on gender. The quantitative variables are presented as mean and standard division (SD), and the Mann–Whitney U test was used to detect the difference in the investigated variables between groups pairwise, including diabetic patients with and without obesity, controls with and without obesity, obese patients and controls, and nonobese patients and controls. Furthermore, Kruskal–Wallis was performed to examine variables among all four groups. In Table χ2 test compared the distribution of the LPL S447X genotypes in patients and controls. Also, association between the LPL S447X variants with the risk of T2DM and obesity was investigated by a multivariable binary logistic regression model, which was adjusted for gender, age, lipid profile, and oxidative stress parameters.Data analysis was carried out by SPSS software , and the significance level was considered at 3χ2 test, the female population was significantly higher than the male population in the four study groups (p = 0.01). The anthropometric assessment showed a significant difference between all groups, T2DM with and without obesity, T2DM and controls, obese patients and controls. Only normal BMI group of T2DM patients and controls had an insignificant difference regarding anthropometric parameters . Normal BMI diabetic patients had significantly higher LDL‐C level (120.7 ± 34.1 mg/dl) than normal BMI controls . However, the mean HDL‐C level was significantly different comparing obese diabetic patients and normal BMI controls, and normal BMI individuals had the highest HDL‐C level (50.9 ± 14.3 mg/dl). The TG level was significantly lower in normal BMI individuals than in obese individuals . However, the TOS level was considerably higher in the first group , and obese diabetic patients had a lower level of TAC than nondiabetic obese controls. The concentration of TOS showed a lower level in nondiabetic controls compared to the patient groups. Furthermore, there was a significant difference between patients with and without obesity. Obese‐diabetic patients had the highest TOS concentration  dramatically. Also, the same result was observed in the MDA parameter that obese‐diabetic patients had the highest MDA level than the other groups of the study (Table The TAC level was compared between obese T2DM patients (3 ± 0.7 nmol/ml) and obese controls (3.3 ± 0.8 nmol/ml) that was significantly different  was detected. Also, considering codominant model in comparison between all diabetic patients and all controls it was detected that in the presence of the SX genotype compared to the SS genotype the risk of T2DM reduced by 74% . In over dominant model comparing all diabetic patients with all controls indicated that in the presence of the SX genotype compared to the SS + XX genotype the risk of T2DM reduced by 75% .Table Also in the assessment of anthropometric, lipid profile, and oxidative stress parameters according to the LPL S447X variants in both codominant (SX vs. SS) and over dominant models (SX vs. SS + XX) in all individuals the SX genotype was associated with significantly lower levels of FBS, and TOS (Table p = 0.03) and obesity by 39% .Table n = 115, 51.3 ± 17.9 mg/dl, p < 0.001) than those with WHR ≥ 0.90 . The same pattern was observed considering obese patients  and obese controls , separately.Considering all individuals, the HDL‐C level was significantly higher comparing all individuals with WHR < 0.90  and even nonobese diabetic individuals. The highest level of anthropometric indices observed in obese diabetic patients. Comparing various genotypes of the LPL S447X indicated the SX genotype significantly decreased FBS, and TOS levels. Also, our findings demonstrated that the LPL SX genotype decreased the risk of T2DM among patients from Kurdistan of Iraq. The result for obesity risk was similar and the SX genotype reduced the risk of obesity. Further, significantly higher level of HDL‐C was detected among individuals with WHR < 0.90 than those with WHR ≥ 0.90. Even though several studies investigated this polymorphism in the Asian population, there is a lack of evidence in the Middle East population. The present study was done in the Kurdish population of Iraq. Further studies on a large‐scale population and the evaluation of the interaction between various polymorphisms related to diabetic dyslipidemia could be helpful for understanding the pathogenesis of T2DM.Gulshan Omar Ahmed: conceptualization; data curation; investigation. Zohreh Rahimi: conceptualization; data curation; formal analysis; methodology; project administration; validation; writing – review & editing. Maryam Kohsari: formal analysis; writing – original draft; writing – review & editing. Ebrahim Shakiba: Validation. All authors have read and approved the final version of the manuscript.The authors declare no conflicts of interest. Zohreh Rahimi is an Editorial Board member of Health Science Reports. and a co‐author of this article. To minimize bias, they were excluded from all editorial decision‐making related to the acceptance of this article for publication.The research followed the tenets of the Declaration of Helsinki. The Ethics Committee of Kermanshah University of Medical Sciences approved this study (IR.KUMS.REC. 1400.140). All participants entered the study after they were fully informed of the process and signed written consent.The lead author Zohreh Rahimi, Maryam Kohsari affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned  have been explained.


N = 40 healthy subjects, who completed the PRLT twice. We focused on how individual metrics are derived, i.e., whether data were partially pooled across participants and whether priors were used to inform estimates. We compared the reliability of the resulting indices across sessions, as well as the internal consistency of a selection of indices. We found good to excellent reliability for behavioral indices as derived from mixed-effects models that included data from both sessions. The internal consistency was good to excellent. For indices derived from computational modeling, we found excellent reliability when using hierarchical estimation with empirical priors and including data from both sessions. Our results indicate that the PRLT is well equipped to measure individual differences in cognitive flexibility in reinforcement learning. However, this depends heavily on hierarchical modeling of the longitudinal data (whether sessions are modeled separately or jointly), on estimation methods, and on the combination of parameters included in computational models. We discuss implications for the applicability of PRLT indices in psychiatric research and as diagnostic tools.Task-based measures that capture neurocognitive processes can help bridge the gap between brain and behavior. To transfer tasks to clinical application, reliability is a crucial benchmark because it imposes an upper bound to potential correlations with other variables . However, the reliability of many task readouts is low. In this study, we scrutinized the retest reliability of a probabilistic reversal learning task (PRLT) that is frequently used to characterize cognitive flexibility in psychiatric populations. We analyzed data from The online version contains supplementary material available at 10.3758/s13428-021-01739-7. The Research Domain Criteria (RDoC) framework was introduced as a stepping stone towards a transdiagnostic understanding of mental health and disease . It is designed to measure flexible behavioral adaptation and is frequently used in the field in a transdiagnostic manner . Reaction times can be analyzed, but this has been done less frequently for PRLTs. Reduced accuracy has been observed in many psychiatric conditions such as schizophrenia  can be harnessed to capture variation in the processes that underlie observed behavior  completed a PRLT  aged 19 to 38 years , participants had 1.5 seconds to choose between two cards that were associated with different probabilities of winning or losing 10 cents . After making their choice, they were shown a feedback screen  for 0.5 seconds. Feedback was drawn at each trial with replacement, i.e., if the card with 80% win probability was chosen, a random number between 0 and 1 was drawn from a uniform distribution—if it fell between 0 and 0.8, the participant received a win feedback; if it fell between 0.8 and 1, the participant received a loss feedback. This procedure meant that the actual ratio of wins and losses associated with the stimuli varied across individuals and sessions (from 73.13% to 86.25%). The feedback screen was followed by a variable inter-trial interval with a mean of 2.5 seconds, in which participants were shown a fixation cross. After an initial acquisition phase  the cards’ reward contingencies flipped five times , such that the previously more lucrative stimulus now became the more frequently losing one, and vice versa. For details, see Fig. In our analysis, we systematically investigated the respective retest reliability of several approaches to processing the task data. In addition, we calculated within-session  reliabilities of the raw behavioral measures.Dependent variable ~ Intercept[*factor] + (Intercept[*factor] |Subject/Session). Because we employ maximal random-effects structures in all our models . In the case of metrics derived from mixed-effects models, we compared estimates from separate models for each session with estimates derived from a single model which accounts for the data from both sessions jointly and includes session as a grouping variable nested within subject , using the lme4 package (version 1.1-21). Results were considered significant at In order to identify individual differences in processes thought to underlie behavior on this task, we fitted different RL models from two families of models. The first model family included models based on Q-learning . The unchosen option is not updated (single update):Here, Learning might be differentially sensitive to wins and losses, resulting in different degrees of value updating after wins versus losses. This can be captured in models with different learning rates for wins and losses.Because the task has an anti-correlated structure, such that if the chosen stimulus yields a win, the other would have invariably yielded a loss, individuals can use this information to simultaneously update the expected values of both the chosen and the unchosen option. This can be captured in a double update (DU) model:Like the original single update (SU) model, this can be extended with separate learning rates for wins and losses. Finally, it is conceivable that individuals use their knowledge of the task structure and perform double updating but do not update the unchosen option as much as the chosen one. This can be captured using a discount weight 𝜅, which attenuates updating of the unchosen option.𝜅 can be added to all DU models, changing only the equations for the unchosen option. In each model, we use a softmax response model to transform values to choice probabilities for each option:The parameter β, the softmax inverse temperature or choice sensitivity, influences the extent to which a difference in values translates into a difference in choice probability by determining the steepness of the softmax sigmoid. If β is large, choices are more deterministic or exploitative; if it is small, choices are more stochastic or explorative, such that differences in values exert less influence on action selection. Like learning, choice stochasticity may be differentially sensitive to wins and losses, resulting in asymmetric staying and switching . This can be captured in separate softmax temperature parameters for trials after receiving wins or losses:The different combinations of parameters—SU or DU, single or separate learning rates and temperature parameters for wins and losses—yield a total of 12 models.In the second model family, we use a reinforcement sensitivity parameter ρ instead of a softmax temperature parameter (dropped in this family) to quantify choice stochasticity:Unlike the inverse temperature parameter, which influences choice stochasticity by determining the steepness of the softmax, the reinforcement sensitivity ρ does this by determining the maximum difference between expected values, thus posing a lower bound to choice stochasticity. The effect on choice probabilities is essentially the same, but the models can have different estimation properties and may differ in their interpretation under certain circumstances ). In MAP estimation, a regularizing prior on 𝜃 is provided, such that the quantity to be maximized becomes ∝ log(p(data| θ) ∗ p(θ)). For MAP0 estimation, we defined an uninformative Gaussian prior with a mean of 0 and variance of 10 (default in the emfit toolbox). For EM-MAP, we used empirical Gaussian priors on our parameters ), inferred from the multivariate distribution of the estimates across subjects in an expectation maximization procedure  in both model families in order to constrain them to their natural range (0 and 1). For models with a single reinforcement sensitivity (𝜌) or softmax temperatures (𝛽), we used an exponential transform to ascertain that they were positive; for models with separate reinforcement sensitivities for wins and losses, the parameters were left in native space. Parameter estimation was performed in MATLAB R2020b using the emfit toolbox  likelihood of the data from each session one at a time, as well as jointly, i.e., maximizing the overall (posterior) likelihood of the data pooled across the two sessions. For the joint estimation, we concatenated the data from both sessions but fitted separate parameters for each session. Concretely, this meant that we fit one set of parameters for the first 160 trials (from session one) and another set for the second 160 trials (from session two), resetting Q-values for the first trial of session two. Thus, both approaches yield separate parameters for the first and second session. However, when estimated jointly, covariances between parameters across sessions are taken into account in a multivariate prior in the case of EM. Note that this does not mean, for example, that the learning rate for sessions one and two shared a prior. Instead, each parameter is accounted for by its own mean and variance in addition to the covariances between parameters.To minimize the risk of local minima, we restarted the optimization 10 times (for EM at each M step) at different random starting points, taking the best iteration forward in the case of EM. In addition, we repeated the estimation procedure 10 times, and used the final results with the maximum (posterior) likelihood for reliability analysis. We performed model selection on the estimated models based on the integrated Bayesian information criterion  against those derived from the original data for visual comparison. Further, we probed the recoverability of the parameter estimates by refitting the models to the generated data and computing the average correlation between the resulting estimates and the underlying true values.In order to show recoverability across the model space, we further extracted the parameters of eight models of varying complexity (four from each model family), and simulated 10 datasets for 38 participants for each model based on the respective algorithms. We then refitted the same eight models to each dataset to probe model and parameter recoverability. In the interest of space, the results of this analysis are reported in the supplement.In order to assess the retest reliability of the behavioral metrics and model-derived parameters, we computed intra-class correlations (ICCs), more specifically ICCs in McGraw and Wong’s  notationget_variance function as part of the insight package for lme4 model fits. Specifically, we took the ratio of the variance explained by the random effect of subject (between-subject variance) and the sum of that variance and the variance explained by session within subject (within-subject variance). Model-derived Pearson correlations between parameters were calculated by dividing the covariance of the equivalent parameters from each session by the product of the square roots of the variances of the individual parameters.We calculated ICCs for means, predicted values, and fitted parameters (i.e. point estimates); however, certain ICCs can also be obtained directly from variances estimated as part of the model  and continuous (reaction time-like) data . Specifically, for each dataset, we simulated normally distributed indices for 38 subjects that were correlated at The data and the scripts underlying the analyses in this article are available on the Open Science Framework (osf.io/4ng3e).As confirmed by a binomial test, two participants performed at chance level (percent correct responses ~ 50%) in one or both sessions. We excluded them from all further analyses for two reasons: first, because their task data may be suggestive of noncompliance; second, and most importantly, because they deviate > 3 standard deviations from the other participants in terms of accuracy. Thus, including their data substantially increases between-subject variance and might thereby inflate reliability estimates in a non-meaningful manner.r = .41) and when sessions were modeled separately  = .42, ICC(1) = .42, r = .41), and good reliability when they were modeled jointly  = .66, ICC(1) = .66, r = .65)  = .41, ICC(1) = .42, 65) Fig. . The modβ = 1.486, z = 24.25, p < .0001), such that participants switched more after losses than wins. The reliabilities of switching overall and after wins and losses respectively differed depending on estimation methods: they were good to excellent when estimated based on the mean proportion of switches overall = .72, ICC(1)overall = .72, roverall = .73; ICCwin = .60, ICC(1)win = .58, rwin = .67; ICCloss = .77, ICC(1)loss = .77, rloss = 0.77); fair to excellent when estimated based on separate models for each session overall = .72, ICC(1)overall = .71, roverall = 0.77; ICCwin = .59, ICC(1)win = .57, rwin = 0.72; ICCloss = .79, ICC(1)loss = .79, rloss = 0.79); and good to excellent when based on the joint model overall = .83, ICC(1)overall = .83, roverall = 0.87; ICCwin = .77, ICC(1)win = .67, rwin = 0.84; ICCloss = .97, ICC(1)loss = .97, rloss = 0.97) win = .69, ICC(1)loss = .92)Mixed-effects logistic regression revealed a main effect of previous feedback on switching (97) Fig. . The modr = .36), poor reliability when sessions were modeled separately  = .25, ICC(1) = .2, r = .28), and good reliability when they were modeled jointly  = .72, ICC(1) = .72, r = .72)  = .35, ICC(1) = .34, β = −0.022, t(37.04) = −6.211, p < .0001), such that people responded faster after positive feedback. The reliability of reaction times overall, after wins and losses respectively, and of the difference between them, differed depending on estimation methods: the reliabilities ranged from fair to excellent when estimated based on average RTs overall = .74, ICC(1)overall = .73, roverall = 0.84; ICCwin = .75, ICC(1)win = .74, rwin = 0.82; ICCloss = .70, ICC(1)loss = .68, rloss = 0.80; ICCdelta = .47, ICC(1)delta = .47, rdelta = 0.48); from fair to excellent when estimated based on separate models overall = .74, ICC(1)overall = .72, roverall = 0.84; ICCwin = .66, ICC(1)win = .74, rwin = 0.83; ICCloss = .77, ICC(1)loss = .68, rloss = 0.82; ICCdelta = .45, ICC(1)delta = .44, rdelta = 0.46); and from good to excellent when estimated based on the joint model overall = .78, ICC(1)overall = .76, roverall = 0.86; ICCwin = .79, ICC(1)win = .78, rwin = 0.86; ICCloss = .75, ICC(1)loss = .74, rloss = 0.85; ICCdelta = .75, ICC(1)delta = .74, rdelta = 0.76) win = .76, ICC(1)loss = .71, ICC(1)delta = .63).Mixed-effects linear regression revealed significant main effects of previous feedback on reaction times (76) Fig. . The modPartitioning the variance of the metrics into within-subject variance (systematic effects of session), between-subject variance, and error variance, we see that the relative amount of error variance decreases depending on the estimation method Fig. . Thus, tUsing simulations , we investigated differences in recoverability of retest reliability between estimation methods for both binary and continuous data. For binary data, we show that means and estimates based on separate modeling of sessions tend to substantially underestimate the true retest reliability, while the estimates based on joint modeling of sessions tend to moderately overestimate it Fig. . Model-cFor continuous data, we show that all estimation approaches yield ICCs that are reasonably close to the true correlations Fig. . SimilarThe behavioral metrics proved to have good or excellent internal consistency  for the first session, with the exception of the perseveration index and the difference in reaction times between wins and losses, whose internal consistency was merely fair before Spearman–Brown correction Fig. .Fig. 6Ir = .84 [.71–.92], rSB = 0.91 [0.83–.96]; stay r = .76 [.58–.87], rSB = 0.86 [0.74–.93]; win stay r = .74 [.54–.85], rSB = 0.85 [0.7–.92]; lose stay r = .70 [.49–.83], rSB = 0.82 [0.66–.91]; perseveration r = .65 [.41–.79], rSB = 0.78 [0.58–.89]; RT r = .97 [.93–.98], rSB = 0.98 [0.97–.99]; win RT: r = .97 [.95–.99], rSB = 0.99 [0.97–.99]; loss RT: r = .90 [.82–.95], rSB = 0.95 [0.90–.97]; delta RT: r = .35 [.03–0.60], rSB = 0.52 [0.07–.75]).In the second session, split-half reliabilities were similarly good to excellent, with the exception of the index quantifying the difference in reaction times after wins and losses, whose internal consistency was merely fair before Spearman–Brown correction  showed that a weighted DU model with separate learning rates and softmax temperatures for wins and losses (DU-2𝜌2𝛼𝜅) had the best evidence, as reported previously , between-subject variance, and error variance, we see that the relative amount of error variance decreases depending on the estimation method Fig. . Thus, tReliabilities were similar whether or not parameters were estimated using analytically supplied gradients, i.e., using a quasi-Newtonian or trust-region algorithm for optimization. In particular, they were exactly equivalent for the best-fitting model as well as a number of other models , and improved further as more data were available for pooling . Given previous research investigating the reliability of cognitive tasks  and the learning rate(s)) Daw, . By contThe reason that we cannot prove that poor identifiability caused low reliability in our complex models is that we cannot perfectly differentiate between the effects of identifiability and goodness of fit. They tend to coincide, so that some simpler models also fit the data better. This highlights a different, though equally important, problem complex models may have: parameters that do not capture meaningful variance in the data will hardly be reliable, even if they are perfectly identifiable. This could well have been an issue with some of our DU models, in particular within the softmax family. Healthy individuals usually perform rather well on the task and win frequently, so the loss-specific parameters may mop up much of the variance usually associated with double updating, causing poorer fit for models containing kappa. Though somewhat sobering, this is not necessarily a quietus for the investigation of the more intricate processes that some complex models reflect—it merely suggests that the specific PRLT we focused on might not be ideal to probe them. It is conceivable that tasks can be designed so as to orthogonalize the parameters to a more workable extent, and it may be well worth it if it comes with the added benefit of sufficient reliability.Though we did not empirically show this, it is important to stress that even given high reliability of the PRLT, associations with other measures such as symptom scores and neuroimaging data will always depend to an equal degree on the reliability of those measures


Conventional Doppler measurements have limitations in predicting left ventricular diastolic dysfunction (LVDD) in patients with mitral regurgitation (MR). Recently, electrocardiographic P‐wave peak time (PWPT) has been proposed as a parameter of detecting LVDD. This study aimed to evaluate the association between PWPT and left ventricular end‐diastolic pressure (LVEDP) in patients with MR.n = 40) and high LVEDP group (H‐LVEDP) . We evaluated LVDD and PWPT based on echocardiographic and electrocardiographic findings in both groups.We performed echocardiography and cardiac catheterization in 82 patients with moderate or severe MR. We classified patients into two groups: low LVEDP group (L‐LVEDP)  was significantly longer in patients in the H‐LVEDP group than in those in the L‐LVEDP group . Using correlation analysis, LVEDP was positively correlated with PWPTII . Using multivariate analysis, PWPTII was found to be an independent predictor of increased LVEDP . Using receiver operating characteristic (ROC) curve analysis, the optimal cutoff value of PWPTII for predicting elevated LVEDP was 58.9 ms, with a sensitivity of 80.0% and a specificity of 73.8% .The PWPT in lead II  and left ventricular end‐diastolic pressure (LVEDP) in patients with MR. LVEDP is positively correlated with PWPTII.Conventional Doppler measurements have some limitations in predicting left ventricular diastolic dysfunction (LVDD) in patients with mitral regurgitation (MR). Electrocardiographic P‐wave peak time (PWPT) has been proposed as a parameter of LVDD. We evaluated correlation between the PWPT in lead II (PWPT This abnormality can develop adverse cardiovascular events, such as heart failure and arrhythmia  is variable and depends on a combination of factors including regurgitation volume, myocardial status, and etiology of the underlying disorder. In asymptomatic patients with primary severe MR, the rate of progression of symptoms including left ventricular (LV) dysfunction, pulmonary hypertension, or atrial fibrillation (AF) is 30%–40% at 5 years Bonow, . Recent Electrocardiography (ECG) is used in the diagnosis and management of cardiovascular diseases. Although there are no specific ECG findings unique to the presence of diastolic dysfunction, certain ECG findings, such as electrocardiographic left ventricular hypertrophy voltage criteria and P‐wave terminal force (PWTF) in lead V1, are associated with LVDD  classification and diagnosis of heart failure were established according to the Framingham criteria , according to the standard imaging techniques recommended by the American Society of Echocardiography (ASE) , left ventricular nadir pressure (diastolic LVP), and LVEDP were measured at rest in a steady state. LVEDP was measured at end‐diastole, which was defined electrocardiographically by the onset of the next cycle's QRS wave from LVP tracing recorded with a 40‐mmHg scale. We classified the patients into two groups: the low LVEDP (L‐LVEDP) group (LVEDP <16 mmHg) and the high LVEDP (H‐LVEDP) group (LVEDP ≥16 mmHg). Aortic pressure was measured with a 4‐Fr catheter or 5‐Fr pigtail catheter via the radial artery or femoral artery. Pulmonary capillary wedge pressure (PCWP), pulmonary arterial pressure (PAP), right ventricular pressure (RVP), and right atrial pressure (RAP) were measured using a 7‐Fr Swan‐Ganz catheter via the jugular or femoral vein.2.4A 12‐lead high‐resolution surface ECG was obtained using an electrocardiograph  in all participants in the supine position with a filter range of 100 Hz, paper speed of 25 mm/s, and amplitude of 10 mm/mV. All measurements were performed by two independent cardiologists, blinded to the patients' clinical information. All patients breathed freely but were not allowed to speak during the ECG recordings. Measurements were performed using a magnifying lens. The QRS duration was defined as the interval from the start of the QRS complex until the J point. The beginning of the P wave was defined as the point at which the initial deflection of the P wave crossed the isoelectric line, and the end of the P wave was defined as the point at which the final deflection of the P wave crossed the isoelectric line. We measured maximal P‐wave duration time, minimal P‐wave duration time, and average P‐wave duration time in each case. PWPT was defined as the duration between the beginning and the peak of the P wave. In the biphasic P‐wave morphology, which was defined as a P wave with positive and negative deflections, or negative P‐wave morphology, PWPT was measured from the beginning of the P wave to the nadir of the negative deflection. In case of the biphasic or negative P‐wave morphology in lead V1, PWTF was calculated by multiplying the amplitude (milliampere) and time (millisecond) of the terminal negative component of the P wave in lead V1  curve analysis was used to calculate the PWPT value that predicted the H‐LVEDP group with the highest specificity and sensitivity.Easy R (EZR) version 1.54  was used to analyze the data Kanda, . The Kol32, p = .05). The cardiac catheterization findings are presented in Table p = .01). Systolic LVP, systolic aortic pressure, diastolic aortic pressure, mean aortic pressure, A‐wave pressure in the PCWP, V‐wave pressure in the PCWP, mean PCWP, systolic PAP, diastolic PAP, mean PAP, systolic RVP, RVEDP, and A‐wave in the RAP were found to be significantly higher in the H‐LVEDP group than those in the L‐LVEDP group (p < .05 in each case). Echocardiographic findings are presented in Table p = .52). There was no significant difference between the two groups in terms of MR severity, primary MR, secondary MR, AS, AR, and TR except of the number of patients with mitral valve prolapse . Left atrial diameter (LAD), LAVI, E/A ratio, septal e′ velocity, E/e′ ratio, and TR peak velocity were similar between the two groups (p > .05 in each case). Table V1 (p > .05), PWPTII in the H‐LVEDP group was significantly longer than that in the L‐LVEDP group . Using the correlation analysis, A‐wave pressure in the PCWP, V‐wave pressure in the PCWP, mean PCWP, and LVEDP were positively correlated with PWPTII . The mean PAP and mean PCWP showed mild positive correlation with PWTF in lead V1 . Univariate logistic regression analyses were performed to determine the clinical variables that associated with increased LVEDP. The clinical variables included body mass index, hemodialysis, paroxysmal AF, moderate or severe AS, LAD, E/e′ ratio, systolic aortic pressure, mean PCWP, average P‐wave duration, PWTF in lead V1, PWPTII, and PWPTV1. Multivariate logistic regression analyses were performed for those variable  that showed significant association with H‐LVEDP. Using multivariate analyses, systolic aortic pressure and PWPTII were found to be independent predictors of H‐LVEDP   and conducted in accordance with the guidelines of the Declaration of Helsinki on February 24, 2021.An opt‐out method on our website was used in the recruitment of participants.Reproduction in any form or by any means, electronic, mechanical, or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission.None.


Chem. Chem. Phys., 2022, 24, 5269–5281, https://doi.org/10.1039/D1CP04393E.Correction for ‘Transfer learned potential energy surfaces: accurate anharmonic vibrational dynamics and dissociation energies for the formic acid monomer and dimer’ by Silvan Käser  The authors would like to make a correction to The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.


AEadverse eventCIconfidence intervalCRcomplete responseHRhazard ratioJCOGJapan Clinical Oncology GroupMPBmelphalan, prednisolone, bortezomibMRDminimal residual diseaseOSoverall survivalPFSprogression‐free survivalPSperformance statusTI‐NDMMtransplant‐ineligible newly diagnosed multiple myelomaWe conducted a randomized phase II study to determine a more promising modified MPB regimen for TI‐NDMM . The primary analysis in JCOG1105 revealed that Arm A (known as PETHEMA/GEM05 MPB) showed a higher CR rate and longer PFS without intolerable toxicities compared with Arm B (a further less intensive MPB) at a median follow‐up period of 26 months, suggesting that the twice‐weekly dosing of bortezomib in the first cycle along with a higher dose of melphalan and higher cumulative dose of both bortezomib and melphalan influenced the efficacy of the modified MPB regimen in patients with TI‐NDMMBetween July 2013 and April 2016, in total 91 patients were randomized to Arm A (45 patients) and Arm B (46 patients). As for the data cut‐off , the median follow‐up period of all eligible patients was 47.3 months (range 10.4–71.1). The PFS rates at 1, 3, and 5‐years (95% CI) were 86.0% (71.6%–93.5%), 27.9% (15.6%–41.6%), and 16.4% (5.8%–31.8%) in Arm A, and 73.3% (57.8%–83.9%), 13.3% (5.4%–24.9%) and not estimable in Arm B with the HR of Arm B to Arm A being 1.69  stage III  and expression of adverse chromosomal translocation‐associated genes , other patient characteristics, treatment exposure including percentage planned dose of bortezomib, melphalan, and prednisolone and incidence of AEs were similar between female and male patients. Among patients with PS 2–3 at study enrollment (10 patients each in both arms), the long‐term PFS in Arm A also tended to be better compared with that in Arm B. As our eligibility criteria permitted the enrollment of patients with PS 3 only resulting from osteolytic lesions (six patients in Arm A and eight patients in Arm B), rapid responses to treatment and improvement of patients' condition could have resulted in better PFS in Arm A.2) compared with in Arm B (252 mg/m2), a lower incidence of second primary malignancies was observed in Arm A (one patient) compared with in Arm B  confers sustained PFS benefit with no new AE‐related concerns. However, a continued risk of relapse was observed in both arms because maintenance therapy was not recommended and all patients except two did not receive maintenance therapy in JCOG1105. Based on the results of this study, we are now conducting a next clinical trial incorporating anti‐CD38 antibody and fixed‐duration maintenance therapy combined with a modified MPB regimen and assessment of high‐risk cytogenetics and MRD .DM, SI, and RM conceived and designed the study; all authors provided study materials and recruited patients; DM, SI, and RM collected, analyzed, and interpreted the data; DM, SI, and RM wrote the manuscript; all authors gave final approval of the manuscript.This work was supported in part by the National Cancer Center Research and Development Funds , a Grant‐in‐Aid for Clinical Cancer Research (H26‐kakushin‐teki‐gan‐ippan‐074) from the Ministry of Health, Labour and Welfare of Japan, and by the Japan Agency for Medical Research and Development under grant nos. JP16ck0106077, JP19ck0106348 and 21ck0106685h0001 (DM).DM reports honoraria ; research funding . SI, an editorial board member of Cancer Science, reports honoraria ; research funding . RM has nothing to disclose. SK reports honoraria ; research funding . NF reports honoraria ; research funding . NY, KM, and MY have nothing to disclose. JK reports honoraria (Janssen). NT and HT have nothing to disclose. KU reports honoraria (Novartis Pharma); research funding . TY reports honoraria (Janssen). TU and IM have nothing to disclose. YT reports honoraria ; scholarship . YN has nothing to disclose. SO reports honoraria ; research funding (Kyowa Kirin). EO has nothing to disclose. IH reports honoraria ; research funding . YS, SY, SY, YS, YK, and SF have nothing to disclose. KT reports honoraria . HN reports honoraria ; research funding ; scholarship (Chugai).Approval of the research protocol: The study protocol was approved by the Protocol Review Committee of JCOG, and was reviewed and approved by the National Cancer Center Hospital Certified Review Board (CRB3180008).Informed consent: Written informed consent was obtained from all the patients.Registry and registration No. of the trial: jRCTs031180097.Animal studies: N/A.Appendix S1Click here for additional data file.


The aim of this study was to identify the factors associated with liver‐related and non‐liver‐related mortality of patients with hepatitis C virus (HCV) after sustained virologic response (SVR) to direct‐acting antiviral agents (DAAs).We conducted a retrospective, single‐center cohort study of HCV patients cured by DAAs.A total of 330 patients with SVR to DAAs were eligible. The median follow‐up period was 3.38 years (inter‐quartile range: 2.03–4.58). The cumulative liver‐related or non‐liver‐related mortality rates at 1, 3, and 5 years were 0.00 or 1.29%, 2.87 or 3.60%, and 5.10 or 9.46, respectively. Among the liver‐related deaths, 9 of the 10 were from liver cancer. Among the non‐liver‐related deaths, the most common cause was malignancy. Through multivariate analysis using the Cox proportional hazard model, diabetes mellitus  and a history of hepatocellular carcinoma , independently predicted liver‐related death. No variables were associated with non‐liver‐related death.Our findings suggest that DM and a history of HCC are risk factors for liver‐related mortality of HCV patients cured by DAAs. These results indicate that early management of HCV and HCC surveillance of diabetic patients after SVR are important to increase the chance of survival. Further studies are needed to confirm the association of DM and HCC history with survival. Very few studies have assessed the factors associated with liver‐related mortality in patients with sustained virologic response (SVR) following direct‐acting antiviral agents (DAA) treatment. The findings of our cohort study are that diabetes mellitus and a history of hepatocellular carcinoma (HCC) are risk factors for the liver‐related mortality of HCV patients cured by DAA. The results indicate that early management of HCV and HCC surveillance of diabetic patients after SVR are important to increase the chance of survival. Hepatitis C virus (HCV) infection is a major cause of death worldwide, mostly through cirrhosis and hepatocellular carcinoma.We recruited patients with chronic hepatitis C who were administered IFN‐free DAA treatment between September 2014 and January 2021 at St. Marianna University Hospital. Among the patients with a history of HCC, those who had achieved a complete radiologic response to HCC treatment had received DAA treatment. Therefore, there was no patient who had active HCC at the time of initiation of the final DAA therapy. We excluded patients who were: (i) under the age of 18; (ii) co‐infected with human immunodeficiency virus or hepatitis B virus; (iii) had other chronic liver diseases, such as autoimmune hepatitis, primary biliary cirrhosis, and alcoholic liver disease, (iv) had no virological outcome ; (v) failed their last DAA therapy; and (vi) had less the 90 days of follow‐up after initiation of DAA therapy. This study was conducted with the approval of the ethics committee of St. Marianna University School of Medicine, in accordance with the ethical standards laid down in the 1975 Declaration of Helsinki. The need for written informed consent was waived because of the retrospective nature of the study. The opt‐out method to obtain patient consent was used in our institution .We collected data at the time of initiation of the final DAA therapy at St. Marianna University Hospital. Details of age, sex, HCV genotype, history of HCC, previous DAA treatment, DM, and hypertension were obtained from the medical records.We collated the results of laboratory tests, such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin, total bilirubin, platelet, alpha‐fetoprotein (AFP), and mac‐2 binding protein glycan isomer (M2BPGi) before the final DAA treatment and at 12 weeks after the end of treatment.9/L) × (ALT [U/L]) 1/2}.The FIB‐4 index was calculated from the following equation: age (years) × AST (U/L)/{platelet counts  with contrast media or gadolinium‐ethoxybenzyl‐diethylenetriamine pentaacetic acid‐enhanced dynamic magnetic resonance imaging (Gd‐EOB‐DTPA‐MRI) was also performed every 3–6 months. HCC was diagnosed using contrast media and imaging modalities, such as dynamic CT or Gd‐EOB‐DTPA‐MRI, or by histological evidence. Treatment of HCC was performed at St. Marianna University Hospital, based on Japanese guidelines.P values for statistical tests were two‐tailed, and values of <0.05 were considered statistically significant. Statistical analyses were performed using JMP Pro 14.2.0  and Prism 5 for Windows .Data for continuous variables are expressed as the median (inter‐quartile range [IQR]). Data for categorical variables are expressed as frequency and percentage. The primary outcomes of this study were the incidence and predictors of liver‐related and non‐liver‐related death of HCV patients cured by IFN‐free DAA treatment. The start date of follow‐up is defined as the date of achieving SVR. We retrieved the cause of death from the medical records. Kaplan–Meier methods were used to identify the cumulative all‐cause, liver‐related, and non‐liver‐related mortality. Univariate and multivariate Cox proportional hazard models were used to identify the predictors of liver‐related and non‐liver‐related death. All A total of 421 patients who started IFN‐free DAA therapy at St. Marianna University Hospital were identified. Fourteen of these patients were excluded because they lacked an SVR assessment. Of these, four patients died during the treatment period or within 12 weeks after the end of treatment. Death was attributable to liver failure in one patient, while the cause of death in the others was liver unrelated . During 1481 person‐years of follow‐up, 25 patients died: 10 due to liver‐related death and 15 due to non‐liver‐related death. The cumulative all‐cause mortality was 1.29, 6.37, and 14.1% at 1, 3, and 5 years. The cumulative liver‐related and non‐liver‐related mortality rates were 0.00, 2.87, and 5.10% and 1.29, 3.60, and 9.46% at 1, 3, and 5 years, respectively . This study also showed that diabetic patients had a 3.45‐fold increased risk of cardiovascular death (P = 0.014).In the Italian cohort, Calvaruso Our study has several limitations. First, the sample size is small and we might not have been able to demonstrate a statistically significant difference in some variables using multivariable analyses. Second, other variables linked to HCC development, including history of obesity and alcohol intake, should have been collected as comprehensively as possible from our patients.In conclusion, the findings of our cohort study are that DM and history of HCC are risk factors for the liver‐related mortality of HCV patients cured by DAA. The results indicate that early management of HCV and HCC surveillance of diabetic patients after SVR are important to increase the chance of survival. Further studies are needed to confirm the association of DM and history of HCC with survival.Figure S1. Cumulative incidence of liver‐related mortality in 284 patients without a history of HCC according to presence or absence of DM.Table S1. Variables associated with non‐liver‐related death of patients with SVR following treatment with DAA.Click here for additional data file.