Candida albicans, the main human fungal pathogen, can cause fungal infection and seriously affect people's health and life. This study aimed to investigate the effects of ritonavir (RIT) on C. albicans and the correlation between SAP2 as well as ERG11 and drug resistance.C. albicans with different drug resistance were measured. M27‐A4 broth microdilution method was used to analyze the drug sensitivity of RIT combined with fluconazole (FCA) on C. albicans. After that, SAP2 and ERG11 mutations were examined by polymerase chain reaction (PCR) and sequencing, and quantitative real‐time PCR was utilized to determine the expression of the two genes. By analyzing pz values, the Saps activity of cross‐resistant strains was the highest, followed by voriconazole (VRC)‐resistant strains, FCA‐resistant strains, itraconazole (ITR)‐resistant strains, and sensitive strains. The pathogenicity of C. albicans in descending order was as follows: cross‐resistant strains, VRC‐resistant strains, ITR‐resistant strains, FCA‐resistant strains, and sensitive strains. With the increase of RIT concentrations, the Saps activity was gradually inhibited. Drug sensitivity results showed that there was no synergistic effect between RIT and FCA. Additionally, no gene mutation sites were found in SAP2 sequencing, and 17 synonymous mutations and 6 missense mutations occurred in ERG11 sequencing. Finally, the expression of SAP2 and ERG11 was significantly higher in the resistant strains compared with the sensitive strains, and there was a positive liner correlation between SAP2 and ERG11 messenger RNA expression .Secreted aspartyl proteinases (Saps) activities and pathogenicity of C. albicans and provide a novel direction for clinical therapeutics of C. albicans infection.These findings may help to improve our understanding of azole‐resistant mechanisms of  These findings may help to improve our understanding of azole‐resistant mechanisms of C. albicans and provide a novel direction for clinical therapeutics of C. albicans infection. C. albicansCandida albicansFCAfluconazoleITRitraconazoleRITritonavirSapssecreted aspartyl proteinasesVRCvoriconazole1Candida albicans is a common opportunistic fungus that usually colonized in the gastrointestinal tract, reproductive tract, mouth and skin, and can cause a variety of diseases, from superficial mucosal diseases to life‐threatening systemic diseases.C. albicans,C. albicans are increasing year by year, which is a major problem in the clinical treatment of C. albicans infection. Therefore, it is vital to explore the pathogenic and drug‐resistant mechanisms of C. albicans, and to search for novel therapeutic strategies.C. albicans has many pathogenic virulence properties, including the formation of biofilms, cell phenotypes transformation, adhesins on the cell surface and the expression of invasive proteases.C. albicans, and play important roles in the infection and pathogenesis of C. albicans.C. albicans.C. albicans infection. A study of Dos SantosC. albicans in human immunodefeciency virus (HIV) patients, and indicated that RIT and Saps belonged to the aspartic protease family, which may be used as a new drug to inhibit Saps. Other studies have reported that 0.1 mg/ml RIT could significantly suppress adhesion of C. albicans to epithelial cells and acrylics,C. albicans.C. albicans.SAP2, considered as a key virulence factor, can provide nutrients for its own growth by degrading macromolecular proteins on the mucosal surface, and simultaneously increase the ability of C. albicans to adhere and invade the host. Our previous research had found that the messenger RNA (mRNA) expression of SAP2 in the itraconazole (ITR)‐resistant strains was higher than that in the ITR‐sensitive strains, which indicated that SAP2 may be associated with the drug resistance of C. albicans.Ergosterol 11 (EGR11).ERG11 and inactivating the target enzymes. However, if ERG11 mutates, the molecular configuration of the target enzyme ERG11p will change, thus reducing the affinity between azole drugs and ERG11p and leading to drug resistant. Suchodolski et al.ERG11 in C. albicans contributed to the increased production of ergosterol, and promoted drug resistance of strains. However, the expression of SAP2 and ERG11 in C. albicans with different drug resistance and the relationship between SAP2 as well as ERG11 and drug resistance remain unclear.C. albicans strains with different drug resistance were used, and the Saps activities in different drug resistant strains were measured in vitro and in vivo. After that, the effects of RIT on Saps activities in C. albicans were determined, and the synergistic effects of FCA combined with RIT on C. albicans were investigated. Further, SAP2 and ERG11 in different drug resistant strains were sequenced, and the relationship between SAP2 as well as ERG11 and drug resistance was explored. These findings will provide a theoretical basis for controlling the occurrence of drug resistance, and provide a new direction for clinical therapeutics of diseases caused by C. albicans.In this study, clinical 22.1C. albicans strains isolated and identified by the Dermatovenereal Fungus Laboratory of The Second Hospital of Shanxi Medical University were used. Among them, there were 10 FCA‐resistant strains, 10 ITR‐resistant strains, 10 voriconazole (VRC)‐resistant strains, 10 FCA, ITR, or VRC‐sensitive strains, and 10 FCA, ITR and VRC cross‐resistant strains. Additionally, standard strain C. albicans ATCC1106, and quality control strains Candida krusei ATCC6258 and Candida parapsilosis ATCC22019 were purchased from the Fungus and Mycosi Research Center, Peking University Medical Science.A total of 50 clinical 2.27 CFU/ml with sterile saline. Afterwards, fungal suspension (5 μl) was inoculated to the center of the BSA agar medium (Beijing Luqiao Technology Co. Ltd), or the BSA agar medium containing 32, 8, 2, and 0 μg/ml RIT. After cultured at 37°C for 72 h, the size of colony ring and surrounding transparent ring were examined. The Saps activity was evaluated by the ratio of colony diameter to diameter of the dense white zone of precipitation around phospholipase (PL) positive colonies .pz value was shown as follows: pz value = (colony diameter)/(colony diameter + the diameter of the transparent ring). The experiment was repeated three times for each strain.Base on the methods of Li et al.2.3n = 10): cross‐resistant group, FCA‐resistant group, ITR‐resistant group, VRC‐resistant group, sensitive group, and control group. The clinical strains with different drug resistance were inoculated in SDA medium, and cultured at 25°C for 48 h. Sterile saline was used to adjust fungal suspension to 1 × 107 CFU/ml. The mice in the cross‐resistant group, FCA‐resistant group, ITR‐resistant group, VRC‐resistant group, and sensitive group were injected with 0.2 ml cross‐resistant fungal suspension, FCA‐resistant fungal suspension, ITR‐resistant fungal suspension, VRC‐resistant fungal suspension, and sensitive fungal suspension, respectively, via tail vein.A total of 60 SPF female mice with 6 weeks old were obtained from the Animal Center of Shanxi Medical University. All mice were free to drink and eat during the experiment. The mice were fed under controlled temperature (24 ± 2°C) and humidity (50 ± 5%) conditions, with a 12 h light/dark cycle. After 3 days of adaptive feeding, the mice were randomly divided into six groups .2.4C. albicans was resuspended in the RPMI1640 medium (Beijing Luqiao Technology Co. Ltd), and the fungal suspension was adjusted to 5 × 103 CFU/ml by the RPMI1640 medium. The C. albicans suspension in a free state was prepared. The preparation of C. albicans suspension under biofilm condition was shown as follows. The fungal suspension was adjusted to 5 × 106 CFU/ml by the RPMI1640 medium. The suspension (100 μl) was added to each well of a 96‐well plate (except the negative control), and cultured at 37°C for 1 h. After washing with phosphate‐buffered saline (PBS) twice, 100 μl RPMI1640 medium was added, and then cultured at 37°C for 24 h. After washing with PBS three times, the medium was discarded, and the fungal suspension was cultured for another 24 h to form mature biofilm.The purified C. albicans was determined using the Clinical and Laboratory Standards Institute (CLSI) standard M27‐A4 broth microdilution method as previously described.C. albicans was firstly established in a 96‐well plate, and different concentrations of FCA or RIT (100 μl) was added. RPMI1640 medium was used as the control. After cultured at 37°C for 48 h, the minimal inhibitory concentration (MIC) values were read based on the CLSI.The sensitivity of RIT combined with FCA on C. ablicans was analyzed based on the values of FICI.FCA in combination/MICFCA alone) + . If FICI ≤ 0.5, FCA and RIT have synergistic effects; if 0.5 < FICI ≤ 1, FCA and RIT have additive effects; if 1 < FICI < 4, there is no interaction between FCA and RIT; if FICI ≥ 4, RIT and FCA have antagonistic effects.The interaction between RIT and FCA on 2.5C. albicans using OMEGA D3370 yeast DNA extraction kit  following the manufacturer's instructions. The primer sequences of ERG11 and SAP2 were shown in Table SAP2/ERG11) for 45 s, and 72°C for 2 min, for a total of 30 cycles, and finally ended at 72°C for 10 min.DNA was extracted from The PCR products were purified and two directional sequenced by Sangon Biotech (Shanghai) Co., Ltd. The gene sequences of the PCR products were analyzed by Blast software, and then were compared with the known sequences in GenBank database (NC032096/X13296). Additionally, the sequences are translated into amino acid sequences to identify gene mutation sites by chromas and secentral softwares.2.6C. albicans. Afterwards, the concentration and quality of total RNA were measured using a microplate reader. Subsequently, RNA was reverse‐transcribed to cDNA using a cDNA synthesis kit  following the manufacturer's recommendations. The primer sequences of ERG11 and SAP2 were shown in Table ERG11 and SAP2 was calculated using the 2‐∆∆Ctmethod.According to the manufacturer's protocols, column yeast total RNA extraction and purification kit (Sangon Biotech (Shanghai) Co., Ltd) utilized to isolate total RNA from 2.7n = 3). SPSS 22.0 software  was utilized for statistical analyses. Data were expressed as mean ± SD) The Log‐rank test in Kaplan–Meier was used to compare the survival time among the groups. One‐way analysis of variance was applied for the comparison of multiple groups. p < .05 was considered as statistical significance. The correlation between SAP2 expression and ERG11 expression was analyzed using Pearson linear correlation coefficient.Each experiment repeated three times  and 6 kinds of missense mutations . As shown in Table For p = .0452).In the sensitive strains, two strains showed synonymous mutations and one strains had missense mutations T495A→D116E). In the drug resistant strains , there were 17 synonymous mutations and 6 missense mutations. Among them, in the single drug resistant strains , 11 of 24 strains showed 3 missense mutations . In the cross‐resistant strains, 7 of 9 strains displayed 6 missense mutations, including D116E, K128T, V488I, T123I, Y132H and A516P. The missense mutation rates of each group were presented in Table E. In the3.5SAP2 in the sensitive, cross‐resistant, FCA‐resistant, ITR‐resistant, and VRC‐resistant strains was 1.715 ± 0.576, 5.380 ± 1.39, 3.879 ± 1.125, 4.385 ± 1.02, and 3.534 ± 1.162, respectively . However, SAP2 expression in the FCA‐resistant strains and VRC‐resistant strains was significantly lower than that in the cross‐resistant strains (p < .05). For ERG11, its expression was markedly higher in the cross‐resistant strains and single drug resistant strains compared to the sensitive strains .The expression of y Figure . CompareSAP2 and ERG11 mRNA expression .The results of Pearson linear correlation coefficient analysis showed that there was a positive liner correlation between 4C. albicans, the main human fungal pathogen, can cause fungal infection and seriously affect people's health and life. In this study, we found that all the 50 clinical strains formed transparent rings, and by analyzing pz values, the Saps activity of cross‐resistant strains was the highest, followed by the VRC‐resistant strains, FCA‐resistant strains, ITR‐resistant strains, and sensitive strains. The results of in vivo mice model showed that the pathogenicity of C. albicans in descending order was as follows: cross‐resistant strains, VRC‐resistant strains, ITR‐resistant strains, FCA‐resistant strains, and sensitive strains. When strains were treated with RIT, the Saps activity was inhibited with the increase of RIT concentrations. Drug sensitivity results showed that there was no synergistic effect between RIT and FCA on C. albicans with different drug resistance. SAP2 sequencing showed that no gene mutation sites were found in all sequencing strains. For ERG11 sequencing, 35 strains had base mutations, including 17 kinds of synonymous mutations and 6 kinds of missense mutations . Additionally, there was a positive liner correlation between SAP2 and ERG11 mRNA expression .C. albicans, 7 C. glabrata, 5 C. parapsilosis, and 12 C. tropicalis were determined, and it was found that 74.56% of the strains detected Saps, 44.73% of the strains detected PL,Candida. Our research showed that 50 clinical strains of C. albicans cultured on BSA medium formed obvious transparent rings, which proved that each strains produced Saps. Price et al.pz value, and proposed that the smaller pz value was, the higher Saps activity was; pz value equalled to 1 meant the strains did not produce Saps. Sanitá et al.C. albicans from the healthy subjects (HS), diabetics (DOC) and non‐diabetics with oral candidiasis (NDOC), and measured the activity of Saps, as well as found that C. albicans from NDOC showed the lower Saps activity compared to the C. albicans from HS and DOC. Our results showed that Saps activity of cross‐resistant strains was significantly higher than that in the sensitive strains. It was speculated that the Saps activity of C. albicans may be associated with the source of strains and their drug resistance. However, the specific reasons should be further explored.In the previous study, the proteases of 37 C. albicans with different drug resistance, an intravenous infection model of systemic candidiasis was established. The mice were injected with fungal suspension by tail vein, and blood culture results proved the model was successfully constructed. In the current study, the median survival time of the cross‐resistant group, FCA‐resistant group, ITR‐resistant group, VRC‐resistant group, and sensitive group was 12, 22, 18, 13.5, and 20 days, respectively. This implied that the pathogenicity of cross‐resistant strains was the highest, followed by VRC‐resistant strains, ITR‐resistant strains, FCA‐resistant strains, and sensitive strains. Schaller et al.C. albicans lacking SAP1 or SAP2 could reduce tissue damage and decrease the expression of proinflammatory cytokines, which indicated that the potential for Saps to cause tissue damage may be related to epithelial‐induced proinflammatory cytokine responses. Another study showed that some Saps, particularly Sap2, could mediate the acute inflammatory response of inflammatory corpus‐dependent vaginal epithelial cells to C. albicans.C. albicans, and it was speculated that higher Saps activity in drug‐resistant strains may enhance the adhesion of C. albicans, promote tissue damage and inflammatory response, resulting in high mortality of mice. However, more studies are needed to be performed to investigate the potential mechanisms.To evaluate the pathogenicity of C. albicans.C. albicans exposed to 100 μmol/L RIT, its adhesion decreased by 50%. In our study, different concentrations of RIT were used to treat with C. albicans, and the pz values were determined. It was obvious that the Saps activity of C. albicans was gradually inhibited with the increase concentrations of RIT. Therefore, we hypothesized that RIT could suppress toxicity of C. albicans by inhibiting Saps activity, and RIT combined with azole drugs (FCA) may have synergistic effects on the inhibition of C. albicans. To verify the hypothesis, drug sensitivity experiments were carried out. The results showed that under free and biofilm conditions, RIT and FCA had no synergistic effects on C. albicans; the additive rates of RIT combined with FCA were respectively 16% and 24%; others displayed no interaction between FCA and RIT. Previous study has indicated that RIT decreased the growth of C. albicans and the activity of Saps in a nitrogen‐limited medium, the yeast carbon base and BSA (YCB‐BSA).C. albicans and the activity of Saps. Therefore, the drug sensitivity assays of RIT combined with FCA are needed to be further performed on the YCB‐BSA medium, or the synergistic effects between FCA and RIT should be confirmed through an animal model.RIT has been reported to be a kind of HIV protease inhibitors, and to directly inhibit Saps of C. albicans, and Sap2 is an important extracellular protease in the Saps family. Whether SAP2 mutates in nature and its relationship with candida albicans resistance to azole drugs are unknown. In this study, SAP2 gene was sequenced, and it was found that there were no gene mutation sites in all sequencing strains. The possible reasons can be that SAP2 gene sequence is relatively conservative and rarely mutates, which may have nothing to do with the drug resistance of C. albicans. However, some scholars believed that the high expression of SAP2 in C. albicans was associated with drug resistance. Copping et al.SAP2 in the FCA‐resistant strains was higher than that in the sensitive strains, which indicated that SAP2 may be related to the drug resistance of C. albicans. Another study demonstrated that upregulation of SAP2 in C. albicans exposed to FCA may be an adaptive mechanism.SAP2 was significantly upregulated in the resistant strains compared with the sensitive strains, and the SAP2 expression was the highest in the cross‐resistant strains. Therefore, we speculated that high expression of SAP2 may be related to the drug resistance of C. albicans.Saps are the primary virulence factor of C. albicans is a complex process involving multiple mechanisms.ERG11 has been proved to play an important role in drug resistance. We sequenced ERG11 gene in the all clinical strains, and found 17 kinds of synonymous mutations and 6 kinds of missense mutations. These missense mutations corresponded to six different amino acids substitutions, including D116E, T123I, K128T, Y132H, V488I, A516P. D116E represented that after T495A missense mutation, the encoded amino acid was changed from aspartic acid to glutamic acid at 116th position. Our study showed that D116E was occurred not only in the sensitive strains, but also in single drug‐resistant strains and cross‐resistant strains, which indicated that D116E may not be connected with drug resistance. The results were in accordance with previous study.C. albicans resistance to FCA, which was inconsistent with our results. Thus, the relationship between K128T mutation and resistance to azoles should be further explored. Manastir et al.ERG11 gene were determined in the FCA‐resistant isolates, and may be associated with the azoles resistance of C. albicans. In addition, a study of Wu et al.ERG11 gene could confer resistance to FCA and VRC. Our results showed that T123I and Y132H substitutions were only found in cross‐resistant strains, which hinted that T123I and Y132H mutations in ERG11 may be lead to cross‐resistance of C. albicans to azoles. A516P appeared in the cross‐resistant strains, and was a new mutation point that was not found in the previous papers. Consequently, further studies are needed to be carried out on the resistance of C. albicans to azoles caused by A516P.Previous study has implied that the drug resistance of ERG11 in C. albicans can promote ergosterol synthesis, thus increasing the drug resistance. However, an in vitro study had found this resistance is reversible, suggesting that the high expression of ERG11 may be an adaptive mechanism in the resistant strains, and not directly related to the resistance of C. albicans to azoles.ERG11 was markedly higher in the cross‐resistant strains and single drug resistant strains compared to the sensitive strains. The results were consistent with the previous reports.SAP2 as well as ERG11 expression and drug resistance. Pearson linear correlation coefficient analysis indicated that there was a positive liner correlation between SAP2 and ERG11 mRNA expression. This implied that ERG11 and SAP2 may have a certain relationship in C. albicans, which jointly lead to the emergence of resistance to azole drugs.In addition, the upregulation of 5C. albicans in the cross‐resistant strains were the highest, and RIT could inhibit the Saps activity. However, there was no synergistic effect between RIT and FCA on C. albicans with different drug resistance. Additionally, the expression of SAP2 and ERG11 was higher expressed in the resistant strains, and there was a positive liner correlation between SAP2 and ERG11 mRNA expression. All these results may help to improve our understanding of azole‐resistant mechanisms of C. albicans and provide a novel direction for clinical therapeutics of diseases caused by C. albicans.In conclusion, the Saps activity and pathogenicity of The authors declare that there are no conflict of interests.Wenli Feng and Jing Yang designed the research. Wenli Feng, Jing Yang, Yan Ma, Zhiqin Xi, and Xiaoqin Zhao did the experiment and obtained the data. Xiaoxia Zhao and Min Zhao analyzed and explained the data. Wenli Feng and Jing Yang drafted the manuscript, and Wenli Feng revised. All authors have read and approved the final version.


Serum ferritin, C‐reactive protein (CRP), lactate dehydrogenase (LDH), and interleukin‐10 (IL‐10) levels were significantly higher in the high TG group compared to the control group. Pearson correlation analysis showed that TG was positively correlated with fasting blood glucose, leukocyte, serum ferritin, LDH, CRP, and IL‐10 levels. Multiple regression showed that serum ferritin and IL‐10 levels affected the TG level (R2 = .095). The TG level in COVID‐19 patients is correlated to serum ferritin and IL‐10 levels, which reflects the activation of macrophages. It is suggested that COVID‐19 patients be monitored for elevated TG as both a prognostic indicator and potential therapeutic target for COVID‐19.The role of triglycerides (TG) in coronavirus disease (COVID‐19) is controversial. The objective of this study was to explore the relationship between TG levels and prognosis in COVID‐19 patients and investigate the factors that affect TG. COVID‐19 patients were divided into normal or high TG level groups. Their demographic data, medical history, signs and symptoms, laboratory results, and final clinical results were analyzed retrospectively. A total of 174 patients were included. TG level was 1.6 (interquartile range [IQR]: 1.1‒2.1) mmol/L for all patients; 2.2 (IQR: 1.8‒2.7) mmol/L and 1.1 (IQR: 1.0–1.3) mmol/L in the high TG and control groups, respectively. Overall, 29 patients (16.7%) died during hospitalization, including 19 (23.1%) in the high TG group and 10 (11.5%) in the control group , log‐rank  The triglycerides (TG) level in coronavirus disease (COVID‐19) patients is correlated to serum ferritin and IL‐10 levels, which reflects the activation of macrophages. It is suggested that COVID‐19 patients be monitored for elevated TG as both a prognostic indicator and potential therapeutic target for COVID‐19. The elderly and people with diabetes, cardiovascular disease, and low immune function are more likely to have severe COVID‐19.Some COVID‐19 patients have high triglyceride (TG) levels.However, the role of TG in COVID‐19 is still unclear. Therefore, we retrospectively studied the characteristics of patients with different TG levels and analyzed the factors that affect TG levels, to preliminarily understand the pathophysiological mechanism of TG in COVID‐19.22.1n = 78) and control group (n = 96). Information for all patients was retrospectively collected through the electronic medical record system, and included demographic characteristics, medical history, symptoms and signs, laboratory test results, SaO2 at the time of admission without oxygen, and final clinical outcomes. Laboratory examination during the course of the disease showed leukocyte and lymphocyte levels were at their minimum values and TG, serum ferritin, C‐reactive protein (CRP), interleukin (IL), tumor necrosis factor (TNF)‐α, lactate dehydrogenase (LDH), erythrocyte sedimentation rate, creatine kinase, and fasting blood glucose levels were at their maximum values. Laboratory tests were carried out early morning in a fasted state, while the examination frequency was subject to disease changes throughout hospitalization. A total of 132 patients were tested for HbA1c, and no glucocorticoid drugs were used before the test. The diagnosis and treatment of COVID‐19 were based on the “New Coronavirus Pneumonia Diagnosis and Treatment Program ”Overall, 186 patients were diagnosed with COVID‐19 from February 9, 2020 to February 29, 2020 in three wards of Wuhan Tongji Hospital, China. All patients were confirmed positive for SARS‐CoV‐2 nucleic acid by quantitative reverse‐transcription polymerase chain reaction. To avoid the effect of drugs on TG levels, 12 patients were excluded . Thus, 174 patients were included in the study. According to “The Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report,”2.2χ2 test was applied to categorical variables. The log rank test was used for survival analysis to test the null hypothesis of no difference between the two groups. Simple regression and multiple linear regression were used to analyze the factors that affect TG. A value of p < .05 was considered statistically significant. All statistical analyses were performed using SPSS 24.0 (IBM).Categorical variables are presented as frequencies and percentages, and continuous variables are presented as median and interquartile range (IQR). One‐way analysis of variance (ANOVA) was used to calculate the difference between groups for continuous variables that fit normal distribution, and non‐parametric tests were used for non‐normally distributed continuous variables. The 3The demographic baseline characteristics of the patients are shown in Table 2 without oxygen inhalation on admission was 95% (IQR: 90–97%) for all patients; 95.0% (88.5–97.2%) and 95.0% (90.0–97.0) in the high TG and control groups, respectively. There was no significant difference between the groups. Overall, 29 patients (16.7%) died during hospitalization, including 19 (23.1%) in the high TG group and 10 (11.5%) in the control group  mmol/L for all patients; 2.2 (IQR: 1.8‒2.7) mmol/L and 1.1 (IQR 1.0–1.3) mmol/L in the high TG and control groups, respectively. Serum ferritin, LDH, CRP, and IL‐10 levels were significantly higher in the high TG group than in the control group  but multivariate regression analysis did not show this correlation.TG levels are affected by insulin resistance‐related diseases (such as type 2 diabetes and impaired fasting glucose), drugs, and diet.4.1This study provides hints that TG metabolism is involved in the pathogenesis of COVID‐19 from a clinical perspective, suggesting that this approach can be targeted for prognosis and treatment. All the patients in this study were hospitalized before the global epidemic of COVID‐19. At that time, there was no effective intervention except oxygen therapy. The pathophysiological characteristics were more easily revealed in these patients as the natural progression of the disease could be analyzed in the absence of additional therapies. In other words, the lack of effective medication at the time presented with a unique opportunity in this small sample size retrospective research to reveal the relationship between TG and prognosis, serum ferritin, and IL‐10.This study has limitations. First of all, this is a small sample size retrospective study conducted in a single geographical area Therefore, the results should be considered as preliminary findings. In particular, the small number of deaths leaves uncertainty whether elevated TG is an independent risk factor for death in COVID‐19 patients. Second, due to special circumstance, these patients could not be followed up long‐term outside the hospital. The follow‐up data is necessary to explore the relationship between inflammation and TG. Thus, larger trials with more prognostic indicators and long‐term follow‐up are needed to determine the relationship between TG and prognosis.5COVID‐19 patients with above normal TG levels have a higher mortality rate. The TG level in COVID‐19 patients is correlated to serum ferritin and IL‐10 levels, which reflects the activation of macrophages. It is suggested that COVID‐19 patients be monitored for elevated TG as both a prognostic indicator and potential therapeutic target for COVID‐19.Peng Zhong and Zhenzhou Wang designed the study, wrote and revised the manuscript. Zhe Du reviewed the manuscript. All authors read and approved the final manuscript.The authors declare that there are no conflict of interests.Ethical clearance for this study was granted by the Ethic Committee of Peking University People's Hospital. Because only the medical records were reviewed, this case series was exempted from signing the informed consent.


There has been no study systematically assessing the causal effects of putative modifiable risk factors on lung cancer. In this study, we aimed to construct a modifiable risk factors atlas of lung cancer by using the two‐sample Mendelian randomization framework.p‐value smaller than 0.05 were considered as suggestive risk factors. While the Bonferroni corrected p‐value for significant risk factors was set to be 8.33 × 10−4.We included 46 modifiable risk factors identified in previous studies. Traits with In this two‐sample Mendelian randomization analysis, we found that higher socioeconomic status was significantly correlated with lower risk of lung cancer, including years of schooling, college or university degree, and household income. While cigarettes smoked per day, time spent watching TV, polyunsaturated fatty acids, docosapentaenoic acid, eicosapentaenoic acid, and arachidonic acid in blood were significantly associated with higher risk of lung cancer. Suggestive risk factors for lung cancer were found to be serum vitamin A1, copper in blood, docosahexaenoic acid in blood, and body fat percentage.This study provided the first Mendelian randomization assessment of the causality between previously reported risk factors and lung cancer risk. Several modifiable targets, concerning socioeconomic status, lifestyle, dietary, and obesity, should be taken into consideration for the development of primary prevention strategies for lung cancer. With the utilization of Mendelian randomization analysis, we provided the evidence for the relationship between previously reported risk factors and lung cancer from the aspect of causation. We identified several modifiable targets for primary prevention of lung cancer, concerning socioeconomic status, lifestyle, dietary and obesity. In the situation when R2 was not provided by the GWAS, we calculated it using data from MRbase.2 of the SNPs, power and F‐statistic were shown in Table 2, power and F‐statistic as the characteristics of the GWAS of traits, which was useful in the sensitivity analysis for weak instrument bias. Finally, we got 60 traits corresponding to the 46 included risk factors, because some risk factors had more than 1 traits  of exposure as proxies. The MR method was based on the following three key assumptions: (A) The IVs is associated with the risk factor (Relevance); (B) The IVs affects the outcome only through the risk factor (Exclusion restriction); and (C) The IVs is not associated with any confounders (Independent).2.5Wald ratio estimate was performed if there was only 1 SNP for the trait, in which SNP‐outcome association was divided by its SNP‐exposure association to obtain the causal relationship.p‐value. Association was considered significant, when p‐value was less than 0.0008 , and considered suggestive, when p‐value was larger than 0.05 / 60 but less than 0.05.Results of the evaluation of causal association were displayed as odds ratio (OR) between the exposure and outcome, as well as its 95% confidence interval (CI) and 2.6Sensitivity analysis was performed to examine if there was any violation of the assumptions of MR or any other potential biases. Specifically, single SNP analysis, leave‐one‐out analysis, MR Egger, funnel plot, WME, and MR‐PRESSO were utilized. Single SNP analysis and leave‐one‐out analysis were conducted to find whether the estimate was driven by single SNP solely. MR‐Egger was used to assess whether there was any directional pleiotropy, to confirm that the genetic IV only affected the outcome through the exposure.2) and sample size were used to calculate the F‐statistic and power.Proportion of variance explained by the genetic IV  in R (version 3.6.1).3We retained 46 putative modifiable risk factors of lung cancer, which were classified into 6 categories, 5 in factors of socioeconomic status (SES), 4 in factors of lifestyle, 29 in factors of dietary, 6 in cardiometabolic factors, 1 in developmental and growth factor, and 1 in inflammatory factor.MR analysis between the 46 putative modifiable risk factors (60 traits included) and lung cancer, LUAD, and LUSC were conducted. MR estimates were presented in Figure 3.1p‐value <0.001], college or university degree , and household income  against lung cancer .MR provided significant evidence for the protective effect of years of schooling  were identified as significant risk factors of lung cancer , 1.34 1.28–1.41); 8–1.41; pr Figure . In WME,r Figure . MR‐Egger Figure . The funr Figure . Rs64935r Figure .It is worth noting that only 1 SNP was available for cigarettes smoked per day, and thus only wald ratio was performed. Single SNP analysis, leave‐one‐out analysis, MR‐Egger, funnel plot, and MR‐PRESSO were not conducted in terms of cigarettes smoked per day. Power of cigarettes smoked per day did not exceed 80%, while F‐statistics and power for time spent watching television were sufficient Table . We did 3.3p‐value <0.001], docosapentaenoic acid (DPA) (22:5n‐3) in blood , eicosapentaenoic acid (EPA) (20:5n‐3) in blood , and arachidonic acid (AA) (20:4n‐6) in blood  and lung cancer , 1.44 (1.01–2.06); p‐value, 0.046], copper in blood , and docosahexaenoic acid (DHA) (22:6n‐3) in blood  were suggestive risk factors of lung cancer. Other PUFA than 18:2 in blood was still a significant risk factor of lung cancer in WME. While in MR‐Egger, the association turned to be suggestive  than 18:2 in blood  Figure . Directi] Figure . While f] Figure . Horizon] Figure . The dis3.5p‐value, <0.001], cigarettes smoked per day , DPA , and AA  , 1.81 (1.02–3.21); p‐value, 0.04], fat intake , serum vitamin B12 , other PUFA than 18:2 , DHA , and EPA .We also analyzed the causal relationship between modifiable risk factors and LUAD and LUSC. The significant relationships with LUAD were discovered in college or university degree  Table . Suggestp‐value, <0.001], college or university degree , household income , cigarettes smoked per day , time spent watching television , and DPA  remained significant risk factors of LUSC after the Bonferroni correction , 1.16 (1.03–1.30); p‐value, 0.01], EPA , AA , BMI , and body fat percentage  were identified as suggestive risk factors of LUSC.Years of schooling [OR (95% CI), 0.40 (0.26–0.62); on Table . Biochemp‐value, 0.01) and circulating adiponectin  and LUAD , two traits of lifestyle (cigarettes smoked per day and time spent watching television), and four traits related to PUFA .p‐value, 0.04). Considering the directional pleiotropy detected, the claimed positive effect of time spent watching television on cigarettes smoked was based on the MR‐Egger estimate. According to the result of MR‐PRESSO, the p‐value of the distortion test between time spent watching television and AA in blood was 0.043. But both the original MR estimate  and the outlier‐corrected MR estimate  suggested no association between them.We found that all the three significant traits of SES were positively correlated with each other in both directions Figure . Years o41, copper and, DHA in blood as well as body fat percentage and increased risk of lung cancer. The bidirectional MR among the significant traits above indicated that they may be intermediate factors of each other.In this study, we analyzed 46 putative modifiable risk factors of lung cancer utilizing MR analysis. We noted that years of schooling, college or university degree, and household income had significant protective effects on lung cancer. This study also provided significant evidence for the positive association of lung cancer with cigarettes smoked per day, time spent watching television, other PUFA than 18:2, DPA, EPA, and AA in blood. We also noted suggestive associations between raised serum vitamin AOur findings show that higher educational attainment and household income decreased lung cancer risk were concordant with the findings of previous conventional observational studies.Among the lifestyle factors analyzed in this study, cigarettes smoked per day and time spent watching television were identified as significant risk factors of lung cancer. The effect of smoking on lung cancer has been well established.Diet and nutrition factors have been attached with great importance. According to the WCRF/AICR report, there is limited evidence to support the role of diet and nutrition in the development of lung cancer, except for arsenic in drinking water and high‐dose beta‐carotene supplements which have convincing evidence.1 and vitamin B12 even had suggestive tendencies to promote the development of lung cancer.The anticancer effect of vitamin supplements is another issue worthy of attention. Our results in combination with previous research did not provide evidence that circulating vitamins had a protective role in lung cancer.The MR analysis suggested a distinct causal effect of BMI and body fat percentage on LUSC and LUAD, with evidence of an increased risk of LUSC and a null relationship with LUAD. This finding is consistent with previous MR studies, and highlighted the histologic‐specific impact of BMI.Our study has several important strengths. We conducted the first two‐sample MR study to systematically draft the modifiable risk factors atlas of lung cancer by using data from large GWAS studies. All 46 risk factors included in this analysis were selected based on our systematic review of previous meta‐analyses and the WCRF/AICR report. The risk factors selected were potentially implicated in lung cancer development with varying degrees of evidence. Many factors have not previously been included in MR analyses of lung cancer, including those proven to be significant risk factors of lung cancer in this study . The use of MR framework can prevent the residual effect of confounders and reverse causality that are commonly present in conventional observational epidemiological studies. Moreover, we displayed the network among significant risk factors and lung cancer for the first time. We also found no significant difference in risk factors between LUAD and LUSC, except for BMI and body fat percentage.2 was not available in the original article, leaving the weak instrument bias unestimated.However, there were still some limitations in this study. First, false negative results may exist in this study, because of the weak instrument bias of some traits, regarding the limited number of SNPs available and R2 and the insufficient power and F‐statistics.5With the utilization of MR analysis, we provided the evidence for the relationship between previously reported risk factors and lung cancer from the aspect of causation. We identified several modifiable targets for primary prevention of lung cancer, concerning socioeconomic status, lifestyle, dietary, and obesity.All authors have no conflicts of interested to declare.Study concept and design: Jiayi Shen, Huaqiang Zhou, Jiaqing Liu, Yan Huang, Li Zhang. Acquisition of data: Jiayi Shen, Huaqiang Zhou, Jiaqing Liu. Analysis of data: Jiayi Shen, Huaqiang Zhou, Jiaqing Liu. Drafting of the manuscript: Huaqiang Zhou, Jiayi Shen, Jiaqing Liu with input of all authors. Critical revision of the manuscript for important intellectual content: All authors.N/A, the need for approval was waived because the present MR analysis was based on anonymous summary data from previous studies.All authors of this paper have read and approved the final version submitted.Fig S1‐5Click here for additional data file.Table S1‐10Click here for additional data file.


Nutritional recovery and hospital readmission following inpatient management of complicated severe acute malnutrition (SAM) are poorly characterised. We aimed to ascertain patterns and factors associated with hospital readmission, nutritional recovery and morbidity, in children discharged from hospital following management of complicated SAM in Zambia and Zimbabwe over 52‐weeks posthospitalization. Multivariable Fine‐Gray subdistribution hazard models, with death and loss to follow‐up as competing risks, were used to identify factors associated with hospital readmission; negative binomial regression to assess time to hospitalisation and ordinal logistic regression to model factors associated with nutritional recovery. A total of 649 children  were discharged to continue community nutritional rehabilitation. All‐cause hospital readmission was 15.4%  over 52 weeks. Independent risk factors for time to readmission were cerebral palsy (adjusted subhazard ratio (aSHR): 2.96, 95% CI 1.56, 5.61) and nonoedematous SAM . Unit increases in height‐for‐age Z‐score (HAZ)  and enrolment in Zambia  were associated with reduced subhazard of time to readmission. Young age, SAM at discharge, nonoedematous SAM and cerebral palsy were associated with poor nutritional recovery throughout follow‐up. Collectively, nonoedematous SAM, ongoing SAM at discharge, cerebral palsy and low HAZ are independent risk factors for readmission and poor nutritional recovery following complicated SAM. Children with these high‐risk features should be prioritised for additional convalescent care to improve long‐term outcomes. Nutritional recovery and hospital readmission following inpatient management of complicated severe acute malnutrition (SAM) are poorly characterised. We aimed to ascertain patterns and factors associated with hospital readmission, nutritional recovery and morbidity, in children discharged from hospital following management of complicated SAM in Zambia and Zimbabwe over 52‐weeks posthospitalization. Nonoedematous SAM, ongoing SAM at discharge, cerebral palsy and low height‐for‐age Z‐score were found to be independent risk factors for readmission and poor recovery following complicated SAM. One‐in‐six children managed for SAM were readmitted into hospital over the first year after discharge and one‐in‐eight remained undernourished by 52 weeks of follow‐up.Nonoedematous SAM, ongoing SAM at the time of discharge and underlying cerebral palsy were independent risk factors for hospital readmission and poor nutritional recovery.Low HAZ was a risk factor for hospital readmission and poor nutritional recovery.Postdischarge care should focus on children with disability, nonoedematous SAM at initial hospitalisation and have ongoing SAM at the time of discharge.Stunting should be considered in the management of children with SAM. RUTF was dispensed until children achieved moderate acute malnutrition (MAM) status on anthropometry; however, in Zimbabwe, there were frequent stock‐outs of RUTF in the urban and peri‐urban areas. In Zimbabwe, once children attained MAM status, RUTF was stopped and no supplementary food was available; caregivers were counselled on using locally available food. In Zambia, children with MAM received High Energy Protein Supplement (HEPS) or Corn Soya Blend and the caregiver was counselled on how to prepare HEPS porridge and the appropriate frequency of feeds, in addition to nutritional counselling on ‘what to give, how much and how often’ according to the Supplementary Feeding Programme for moderately malnourished children.Children were seen in a dedicated study clinic at 2, 4, 12, 24 and 48 weeks postdischarge, with window periods around each time‐point. Endline visits, therefore, occurred at a median of 49.3  weeks postdischarge, with 21.3% of attending children seen by 48 weeks and 84.2% of children seen by 52 weeks. Endline visits for all analyses were censored at 52 weeks.Anthropometric measurements were undertaken using standardised methods at study enrolment, hospital discharge and at each of five scheduled follow‐up visits. Nutritional status was classified as adequately nourished (WHZ ≥ −2 and/or MUAC ≥ 125 mm), MAM (WHZ between ‐2 and −3 and/or MUAC of 115–124 mm) and SAM  .A pre‐specified analysis plan is available at 2.5.1p < 0.25 were included in a multivariable competing risks regression model together with a set of pre‐specified variables that were forced to remain in the model: age, sex, country, oedema on admission, SAM at discharge, cerebral palsy and HIV status. Variables with p < 0.05 in the final model were deemed associated factors.Fine‐Gray subdistribution hazard models were used to estimate the hazard ratios for hospital readmission, with death and loss to follow‐up as competing risks. We also ran a Cox proportional hazards model, assuming no competing risks, as a sensitivity analysis. Cumulative incidence curves were used to describe the time to first hospital readmission. The selection of exposure variables was based on biological plausibility. The full list of variables is provided in Supporting Informations. Strength of association with time to first hospital readmission was initially assessed using univariable analyses. Variables significant at 2.5.2Negative binomial regression models which account for overdispersion were used to estimate the incidence rate of readmissions. Risk factors for readmission were identified using the same approach as for the time to first hospital readmission.2.5.3The following morbidity outcomes were assessed at each study visit by caregiver recall: fever, diarrhoea, respiratory symptoms (defined as cough or difficult breathing), poor appetite and weight loss. The period prevalence of illness episodes was estimated as the proportion of children with any caregiver‐reported symptom in the 2 weeks preceding each visit, stratified by HIV status.2.5.4Nutritional status was assessed at five‐time points postdischarge using regression models with WHZ and MUAC as outcomes. Ordinal logistic regression was also used to identify factors associated with nutritional recovery, defined as moving from a lower to a higher nutritional category . Exposure variables were chosen in the same way as those for hospitalisation.2.6Ethical approval was obtained from the University of Zambia Biomedical Research Ethics Committee, and the Medical Research Council of Zimbabwe. The ethics committee of the Queen Mary University of London provided an advisory review.33.1A total of 755 children with SAM were enroled between 13 July 2016 and 31 March 2018; Figure 3.2p < 0.001) and were less likely to be on TB medication  but were otherwise similar to the follow‐up cohort . Of these, 89  were readmitted through Week 52, with 52.8% of admissions occurring in the first 12 weeks after discharge Figure . Time to3.5p = 0.001] at Week 4, driven particularly by differences in weight loss and appetite, but groups were otherwise similar during follow‐up. There was a significant interaction between HIV status and reported weight loss [OR: 0.71  p = 0.004] as well as diarrhoea [OR: 0.83  p = 0.036] over time.Nearly half (48.8%) of children had at least one caregiver‐reported symptom of ill‐health within 4 weeks of discharge, with a gradual decline at later follow‐up visits. Fever and respiratory illnesses were the most commonly reported symptoms overall; both declined markedly between 24 and 52 weeks. There were more caregiver‐reported symptoms in HIV‐positive versus HIV‐negative children [63.9% [ vs. 44.7% ; 3.6p = 0.003) and Week 4 (p = 0.02), but no significant difference between groups from Week 12 onwards.We next looked at the nutritional status over the follow‐up period Figure . At the Table We identified six independent factors associated with moving from a lower to a higher nutritional status in the multivariable ordinal logistic regression model Table . Two fac4Children discharged from the hospital after the management of complicated SAM continue to be at high risk of illness, hospital readmission and poor nutritional recovery. In this longitudinal study of children followed up after hospital discharge for SAM at three sites in southern Africa, one‐in‐six were readmitted into hospital over the first year postdischarge. Children had frequent illness episodes, and one‐in‐eight children remained undernourished by 52 weeks postdischarge. We identified four consistent risk factors for hospital readmission and nutritional recovery over 1 year of follow up: nonoedematous SAM at hospitalisation, ongoing SAM at discharge, discharge HAZ and underlying cerebral palsy. Contrary to our initial hypothesis, and despite our previous finding that it is a predictor of postdischarge mortality (Bwakura‐Dangarembizi et al., WHO guidelines recommend that complicated SAM should be managed in hospital, followed by referral to a community‐based nutritional rehabilitation facility (World Health Organisation, This study reports the pattern of illness during follow‐up after complicated SAM. Consistent with other studies, fever, respiratory illness and diarrhoea were the most commonly reported symptoms postdischarge (Ashraf et al., At the time of hospital discharge, 44% of children had ongoing SAM. Most studies on nutritional recovery evaluate children after discharge from community‐based therapeutic feeding centres, thus missing the immediate postdischarge period (Khanum et al., Our study showed that HIV infection slowed nutritional recovery in the early weeks following hospital discharge; at Weeks 2 and 4 postdischarge children with HIV had a lower odds of nutritional recovery, but this effect was not seen from Week 12 onwards. Overall our findings are similar to a study by Fergusson et al.  from MalOur study has strengths and limitations. We achieved relatively high rates of follow‐up among this highly mobile and vulnerable population compared to other studies, through phone calls, text messages, free study clinics and home visits. However, we were not able to collect anthropometry and morbidity data on 15.8% of the study population at 52 weeks, which could have biased our findings. Where there were missing risk factors for analysis, imputation led to no major changes in the inferences obtained. We relied on caregiver‐reported data for children who were hospitalised at nonstudy sites, which may have underestimated the proportion of children readmitted. A major limitation was that we did not systematically record nutritional rehabilitation provided in the community. We collected data on the home environment but we may not have captured key factors associated with clinical outcomes such as feeding practices, household food security and caregiver health‐seeking behaviours. An in‐depth social science study of the same population is underway to address this issue. Relapse of malnutrition may have been underestimated as we were only able to capture events at the time of the study visits. For example, it is well known that nutritional oedema can be transient and mild cases could have resolved between study visits (Alvarez et al., Few longitudinal studies have defined morbidity and nutritional recovery of children discharged from hospital for complicated SAM. Here, we found high rates of hospital readmission and caregiver‐reported illness in the first 6 months after discharge, and failure to achieve nutritional recovery by 1‐year postdischarge in 12% of the cohort. The current study, together with our previous findings on mortality in the same cohort, identify a small number of consistent risk factors for adverse outcomes following discharge from hospital: HIV, nonoedematous SAM, cerebral palsy and ongoing SAM at the time of discharge (Bwakura‐Dangarembizi et al., The authors declare that there are no conflict of interests.Mutsa Bwakura‐Dangarembizi, Beatrice Amadi, Kusum J. Nathoo, Shane A. Norris, Paul Kelly and Andrew J. Prendergast designed and oversaw the research study. Mutsa Bwakura‐Dangarembizi, Beatrice Amadi, Claire D. Bourke, Ruairi C. Robertson, Kusum J. Nathoo, Paul Kelly and Andrew J. Prendergast secured funding for the study. Mutsa Bwakura‐Dangarembizi, Kanta Chandwe, Chanda Kapoma, Beatrice Amadi, Deophine Ngosa and Florence D. Majo collected the data. Mutsa Bwakura‐Dangarembizi, Andrew J. Prendergast, Cherlynn Dumbura, Bernard Chasekwa, Jonathan P. Sturgeon, Claire D. Bourke, Ruairi C. Robertson and Robert Ntozini cleaned, analysed and interpreted the data. Mutsa Bwakura‐Dangarembizi and Andrew J. Prendergast wrote the first draft of the paper. All authors critically revised the manuscript.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.


Undernutrition is associated with frailty, one of the common reasons for seeking long‐term care. In older adults, nutritional status is known to be associated with oral function; however, it is not yet known if there is a similar association in middle‐aged adults. The present study determined the association between nutritional status and oral function in middle‐aged adults.This cross‐sectional study was conducted in 117 outpatients aged 40–64 years who underwent dental check‐up at a private dental clinic. Factors associated with oral function, including oral moisture, number of teeth present, oral diadochokinesis, tongue pressure, lip‐seal strength, and masticatory performance, were evaluated. Body mass index (BMI), fat‐free mass index (FFMI), and skeletal muscle mass index (SMI) were analyzed as nutritional statuses. Pearson's correlation analysis was conducted to determine the correlation between nutritional statuses and other variables. Multiple linear regression analysis was performed, with adjustment for age and sex, using BMI, FFMI, or SMI as response variables and oral moisture, maximum tongue pressure, lip‐seal strength, oral diadochokinetic rate, and masticatory performance as explanatory variables.All nutritional statuses showed significant correlation with maximum tongue pressure and lip‐seal strength. Multiple linear regression analysis revealed that BMI was associated with maximum tongue pressure and lip‐seal strength, whereas FFMI and SMI showed significant association with sex, maximum tongue pressure, and lip‐seal strength.In middle‐aged outpatients at a regional dental clinic, lip‐seal strength and tongue pressure were positively associated with BMI, FFMI, and SMI. Patients with the following conditions were excluded (exclusion criteria): acute dental and oral problems, diabetes mellitus, endocrine disease, inflammatory disease , gastrointestinal disease , and dysphagia .2.3Three certified examiners evaluated the factors associated with the oral function and nutritional status of the subjects. We prepared the measurement protocol, trained each examiner accordingly, and confirmed that the same measurements were performed by each examiner.2.3.1Age and sex were considered as demographic variables.2.3.2We adopted oral moisture as oral secretory function, whereas the movement and force of the tongue and lip, the number of remaining teeth as an alternative index of occlusal force, masticatory performance, and swallowing function regarded as oral motor functions were adopted to assess oral hypofunction. Medical equipment typically used for the measurement in Japan was used to evaluate each function. Subjects using removable dentures wore them during the measurements.Oral moisture was measured using an oral moisture‐checking device  from body weight/height2  from FFM/height2  from SM/height2 , body weight (kg), fat‐free mass (FFM) (kg), and skeletal muscle mass (SM) (kg) were measured using InBody J10 , which is capable of performing body composition analysis using Multi‐frequency bioelectrical impedance analysis (MF‐BIA)  was used to perform all statistical analyses  was 36.9 ± 7.9 kPa, and the lip‐seal strength (mean ± SD) was 13.6 ± 3.9 N in men and 12.0 ± 3.1 N in women. Of the 117 subjects, SMI was lower than the cutoff value in 23 subjects (19.6%).No patient was diagnosed with dysphagia via the repetitive saliva swallowing test (RSST). Table 2 and that for FFMI are <15 kg/m2 (women) and <17 kg/m2 (men). Of the 117 subjects, BMI was lower than the cutoff value in 9 subjects (7.7%) and FFMI was lower than the cutoff value in 36 subjects (30.8%).The scatter plots showing the association between BMI and FFMI are presented in Figure 3.2Table Table 44.1Multiple linear regression analysis of oral functions revealed that tongue pressure and lip‐seal strength, which are oral motor functions, were associated with nutritional status. Tongue pressure plays a vital role in bolus formation, and its reduction has been reported to be associated with decreased meal consumption . Educational level of the subjects may affect nutritional intake. However, the impact of SES in this study may be small because health insurance covers dental treatment in Japan. In several previous studies, BMI alone has been used to evaluate nutritional status (Ikebe et al., A history of cancer or deliberate weight loss may also affect BMI, FFMI, and SMI. Particularly, several middle‐aged adults intentionally lose weight. In the present study, none of the subjects had a history of cancer. Furthermore, we did not investigate the presence or absence of weight loss and intentional weight loss; hence, their effects on the results observed in this study remain unclear.4.4We found that tongue pressure and lip‐seal strength were associated with nutritional status of middle‐aged patients, even though the causal relationship was unclear because of the cross‐sectional study design and the fact that the subjects were from one dental clinic. Previous studies investigating the association between oral function and nutritional status included not only cross‐sectional studies but also quasi‐experimental studies and some cohorts, and a few factors associated with oral function were adopted by considering the association between oral function and nutritional status (Bertoli et al., 5Lip‐seal strength and tongue pressure were positively associated with BMI, FFMI, and SMI in middle‐aged patients of a regional outpatient dental clinic.The authors declare no conflict of interest.Tomotaka Nishi was involved in data collection and data analysis. Midori Ohta, Takayuki Ueda and Kaoru Sakurai were involved in study design and data interpretation. Tomofumi Takano and Koichiro Ogami were involved in data analysis and critical review. All authors critically revised the manuscript, commented on drafts of the manuscript, and approved the manuscript to be published, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.