Figure 1.
Flow chart of the present study.
Citation: Mantana Chudtong, Andrea De Gaetano. A mathematical model of food intake[J]. Mathematical Biosciences and Engineering, 2021, 18(2): 1238-1279. doi: 10.3934/mbe.2021067
| [1] | Tongmeng Jiang, Pan Jin, Guoxiu Huang, Shi-Cheng Li . The function of guanylate binding protein 3 (GBP3) in human cancers by pan-cancer bioinformatics. Mathematical Biosciences and Engineering, 2023, 20(5): 9511-9529. doi: 10.3934/mbe.2023418 |
| [2] | Xiaoyu Hou, Baoshan Ma, Ming Liu, Yuxuan Zhao, Bingjie Chai, Jianqiao Pan, Pengcheng Wang, Di Li, Shuxin Liu, Fengju Song . The transcriptional risk scores for kidney renal clear cell carcinoma using XGBoost and multiple omics data. Mathematical Biosciences and Engineering, 2023, 20(7): 11676-11687. doi: 10.3934/mbe.2023519 |
| [3] | Yong Luo, Xiaopeng Liu, Jingbo Lin, Weide Zhong, Qingbiao Chen . Development and validation of novel inflammatory response-related gene signature to predict prostate cancer recurrence and response to immune checkpoint therapy. Mathematical Biosciences and Engineering, 2022, 19(11): 11345-11366. doi: 10.3934/mbe.2022528 |
| [4] | Sufang Wu, Hua He, Jingjing Huang, Shiyao Jiang, Xiyun Deng, Jun Huang, Yuanbing Chen, Yiqun Jiang . FMR1 is identified as an immune-related novel prognostic biomarker for renal clear cell carcinoma: A bioinformatics analysis of TAZ/YAP. Mathematical Biosciences and Engineering, 2022, 19(9): 9295-9320. doi: 10.3934/mbe.2022432 |
| [5] | Yujia Xi, Liying Song, Shuang Wang, Haonan Zhou, Jieying Ren, Ran Zhang, Feifan Fu, Qian Yang, Guosheng Duan, Jingqi Wang . Identification of basement membrane-related prognostic signature for predicting prognosis, immune response and potential drug prediction in papillary renal cell carcinoma. Mathematical Biosciences and Engineering, 2023, 20(6): 10694-10724. doi: 10.3934/mbe.2023474 |
| [6] | Yutao Wang, Qian Shao, Shuying Luo, Randi Fu . Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas. Mathematical Biosciences and Engineering, 2021, 18(6): 8084-8095. doi: 10.3934/mbe.2021401 |
| [7] | Yan He, Nannan Cao, Yanan Tian, Xuelin Wang, Qiaohong Xiao, Xiaojuan Tang, Jiaolong Huang, Tingting Zhu, Chunhui Hu, Ying Zhang, Jie Deng, Han Yu, Peng Duan . Development and validation of two redox-related genes associated with prognosis and immune microenvironment in endometrial carcinoma. Mathematical Biosciences and Engineering, 2023, 20(6): 10339-10357. doi: 10.3934/mbe.2023453 |
| [8] | Peiyuan Li, Gangjie Qiao, Jian Lu, Wenbin Ji, Chao Gao, Feng Qi . PVT1 is a prognostic marker associated with immune invasion of bladder urothelial carcinoma. Mathematical Biosciences and Engineering, 2022, 19(1): 169-190. doi: 10.3934/mbe.2022009 |
| [9] | Jianpei Hu, Zengnan Mo . Dissection of tumor antigens and immune landscape in clear cell renal cell carcinoma: Preconditions for development and precision medicine of mRNA vaccine. Mathematical Biosciences and Engineering, 2023, 20(2): 2157-2182. doi: 10.3934/mbe.2023100 |
| [10] | Alex Viguerie, Sangita Swapnasrita, Alessandro Veneziani, Aurélie Carlier . A multi-domain shear-stress dependent diffusive model of cell transport-aided dialysis: analysis and simulation. Mathematical Biosciences and Engineering, 2021, 18(6): 8188-8200. doi: 10.3934/mbe.2021406 |
Renal cell carcinoma (RCC) is a genitourinary type of carcinoma, accounting for more than 2% of cancer-associated deaths worldwide [1,2]. KIRC is the most common subtype of RCC [3]. Presently, surgical resection is the major therapy of KIRC patients, after which patients with KIRC still have a quite large risk of cancer recurrence and metastasis [4]. In fact, more than 30% of patients who are diagnosed with KIRC experience metastasis, leading to a poor five-year survival rate [5]. It is commonly recognized that the patients with metastatic KIRC have a very dismal prognosis mainly due to the failure of early diagnosis and resistance to chemoradiotherapy [6]. it is urgently needed to find sensitive and reliable biomarkers for individualized prediction of KIRC patients.
Previous studies have reported that specific molecules could serve as survival-associated KIRC hallmarks. For example, Chen et al. suggested that a 3-mRNA predictor was proved to be a useful model for prognostic evaluation and could improve personalized management of KIRC patients [7]. Guan et al. identified a biomarker in KIRC based on miRNA-seq and digital gene expression-seq data [8]. A study revealed the prognostic value of a long non-coding RNA signature in localized clear cell renal cell carcinoma [9]. A previous research reported that fibroblast activation protein predicted prognosis in KIRC [10]. In the process of exploring prognostic markers of cancer, accumulating studies reported that epigenetic modification could affect key cancer-related genes, which implied its significant role in carcinogenesis. For instance, a study suggested that epigenetic therapeutics served as a new weapon in the war against cancer [11]. A previous research revealed their study in the cancer epigenome for therapy [12]. A research reported the interacting cancer machineries: cell signaling, lipid metabolism, and epigenetics [13]. Presently, DNA methylation is significantly involved in epigenetic regulatory mechanism which is intensively studied. Alters in DNA methylation may impact gene expression and interplay with numerous feedback mechanisms [14]. Thus, DNA methylation is a key factor for cancer development and various studies has suggested that DNA methylation may function as a predictor for diagnosis and prognosis of cancer. For instance, Brock et al. revealed DNA methylation markers in stage I lung cancer [15]. Shen et al. suggested that seven-DNA methylation CpG-based prognostic signature coupled with gene expression may predict survival of oral squamous cell carcinoma [16]. Gündert et al. indicated that genome-wide DNA methylation analysis revealed a prognostic classifier for non-metastatic colorectal cancer [17]. It has been reported that DNA methylation profiling is highly accurate and reproducible even using small specimens and poor quality material [18]. Therefore, the analysis of DNA methylation is promising to uncover novel biomarkers for predicting prognosis of cancer. However, few studies have explored the ability of DNA methylation for prognostic prediction in patients with KIRC. It's very promising to build a methylomics-related signature for predicting prognosis of KIRC.
In present study, an integrative analysis was carried out for the prediction of KIRC patients' prognosis and a 9 DNA methylation signature-based classifier was developed using univariate, LASSO, and multivariate Cox regression model. Next, we used Kaplan-Meier analysis, ROC analysis to assess and validate the prediction accuracy of the classifier in internal validation set, external validation set and entire TCGA set. In addition, a high prognostic predicted power of our nomogram was proved based on the training set and internal validation set.
The KIRC DNA methylation information and relevant clinical information was downloaded from TCGA database by using R TCGAbiolinks package [19]. The KIRC DNA methylation data was measured by using Illumina Infinium HumanMethylation450 BeadChip (450 k) arrays based on the manufacturer's instructions. E-MTAB-3274 was retrieved from ArrayExpress database using ArrayExpress package [20]. The DNA methylation levels were set as β values, computed as M / (M + U + 100), with U standing for an unmethylated signal and M standing for a methylated signal. After removing the patients without survival data, the remaining 282 patients with complete DNA methylation data and clinical information were used for constructing prognostic model. The 282 patients were grouped into a training group (70%, n = 198 samples) to identify a prognostic classifier and an internal validation group (30%, n = 84) to validate the predictive performance of the classifier. On the other hand, the 102 KIRC patients of E-MTAB-3274 from ArrayExpress database were employed as an external validation dataset. LASSO COX regression analysis was employed to further determine the candidate methylation sites involved in KIRC patients' RFS. In addition, LASSO analysis was executed by using 1000 iterations on the basis of a publicly available R package "glmnet" [21].
Pre-processing of the raw data was implemented for the determination of a prognostic classifier of KIRC. First of all, Raw methylation data was filtered for selecting the probes in at least one sample. After that, the normalization of the data was executed with "betaqn" function of wateRmelon package [22]. Subsequently, the total KIRC samples were divided into two cohorts (recurrence and no recurrence cohort) in accordance to recurrence status. The standardized beta was transformed to M value on the basis of the formulation: M = log (β/(1-β)). M value was applied for the elimination of the variance which resulted from multiple probes. Finally, M value was exploited for the determination of the differentially expressed methylation sites between recurrence and no recurrence cluster according to "dmpFinder" function of minfi package [23].
In the training set, the differentially expressed methylation sites were analyzed by a univariate Cox proportional hazards regression analysis to screen the promising sites that were importantly associated with RFS of KIRC patients. Then, those sites with p-values < 0.05 in the univariate Cox proportional hazards regression model were used for the LASSO Cox regression analysis to extract candidate sites tightly related to KIRC patients' RFS. Finally, the multivariate Cox regression analysis was performed by the candidate sites for selecting the methylome-based predictor of KIRC patients' RFS. As a result, 9 DNA methylation sites were employed to develop the methylation-based prognostic predictor. Then a risk-score model was constructed by using the 9-DNA methylation signature to compute risk scores of all of the KIRC patients. According to this risk score tool, we separated patients into high and low-risk groups by using a cutoff risk score of the median risk score. We conducted ROC analysis to assess the power of the 9-DNA methylation signature. AUC value was calculated in ROC analysis to weigh the predicted accuracy of methylome-based predictor for KIRC patients' RFS with the "survivalROC" package [24], the larger of the AUC value, the better for the predicted accuracy of the signature. Kaplan–Meier survival was exploited to compare differences in RFS between high- and low-risk clusters and Kaplan–Meier curves were obtained across the "survival" package [25].
Single sample gene sets enrichment analysis (ssGSEA) was conducted based on TCGA KIRC mRNA dataset with GSVA package [26] to assess the 9-DNA methylation signature-relevant signaling pathways. The most important pathway positively correlated with risk score was measured. According to this risk score model, we separated patients into high and low-risk groups by using a cutoff risk score of the median risk score. A p value of < 0.05 was considered as statistically significant.
The univariate and multivariate Cox proportional hazard analysis were conducted via the methylation risk score and other clinic-related variables. Cox proportional hazard models was exploited to measure hazard ratios (HR) and corresponding 95% confidence interval (CI) of variables. Factors that were significant (P < 0.05) from the multivariate Cox proportional hazard analysis were applied for constructing a nomogram by using the 'rms' R package. The prognostic nomogram was developed in TCGA-KIRC dataset for predicting 1, 3, 5-year's RFS of KIRC patients, respectively. C-index, ROC and calibration plot and decision curve analysis (DCA) were applied to evaluate prognostic nomogram performance. The results of the nomogram were presented in the calibrate curve, and the 45° line refers to the ideal prediction.
Totally, 282 TCGA patients and 102 ArrayExpress database patients who were clinically and pathologically diagnosed as KIRC were included in this study. A flow chart of the study procedure was developed to summarize our study (Figure 1). The clinical feature of KIRC patients from TCGA dataset and ArrayExpress dataset was summarized in Table 1.
| Characteristics | Total (n=282) | Training dataset (n=198) | Testing dataset (n=84) |
| Gender | |||
| FEMALE | 104(36.88) | 73(36.87) | 31(36.9) |
| MALE | 178(63.12) | 125(63.13) | 53(63.1) |
| Age | |||
| ≤ 60 | 132(46.81) | 94(47.47) | 38(45.24) |
| > 60 | 150(53.19) | 104(52.53) | 46(54.76) |
| Stage | |||
| Stage I | 147(52.13) | 101(51.01) | 46(54.76) |
| Stage II | 29(10.28) | 19(9.6) | 10(11.9) |
| Stage III | 68(24.11) | 53(26.77) | 15(17.86) |
| Stage IV | 36(12.77) | 23(11.62) | 13(15.48) |
| Not Available | 2(0.71) | 2(1.01) | |
| M | |||
| M0 | 238(84.4) | 168(84.85) | 70(83.33) |
| M1 | 34(12.06) | 22(11.11) | 12(14.29) |
| Not Available | 10(3.55) | 8(4.04) | 2(2.38) |
| T | |||
| T1 | 149(52.84) | 101(51.01) | 48(57.14) |
| T2 | 37(13.12) | 24(12.12) | 13(15.47) |
| T3 | 93(32.98) | 71(35.86) | 22(26.19) |
| T4 | 3(1.06) | 2(1.01) | 1(1.19) |
| N | |||
| N0 | 119(42.2) | 85(42.93) | 34(40.48) |
| N1 | 8(2.84) | 6(3.03) | 2(2.38) |
| Not Available | 155(54.96) | 107(54.04) | 48(57.14) |
| Grade | |||
| G1 | 8(2.84) | 7(3.54) | 1(1.19) |
| G2 | 122(43.26) | 85(42.93) | 37(44.05) |
| G3 | 110(39.01) | 80(40.4) | 30(35.71) |
| G4 | 38(13.48) | 23(11.62) | 15(17.86) |
| Not Available | 4(1.42) | 3(1.52) | 1(1.19) |
| Cancer_Status | |||
| TUMOR FREE | 201(71.28) | 144(72.73) | 57(67.86) |
| WITH TUMOR | 59(20.92) | 38(19.19) | 21(25) |
| Not Available | 22(7.80) | 16(8.08) | 6(7.14) |
| Laterality | |||
| Bilateral | 1(0.35) | 1(0.51) | |
| Left | 130(46.1) | 92(46.46) | 38(45.24) |
| Right | 151(53.55) | 105(53.03) | 46(54.76) |
| Hemoglobin_result | |||
| Elevated | 4(1.42) | 2(1.01) | 2(2.38) |
| Low | 132(46.81) | 86(43.43) | 46(54.76) |
| Normal | 102(36.17) | 75(37.88) | 27(32.14) |
| Not Available | 44(16.60) | 35(17.68) | 9(10.71) |
| Platelet_qualitative_result | |||
| Elevated | 14(4.96) | 10(5.05) | 4(4.76) |
| Low | 30(10.64) | 24(12.12) | 6(7.14) |
| Normal | 189(67.02) | 125(63.13) | 64(76.19) |
| Not Available | 49(17.38) | 39(19.7) | 10(11.9) |
| Serum_calcium_result | |||
| Elevated | 4(1.42) | 2(1.01) | 2(2.38) |
| Low | 100(35.46) | 65(32.83) | 35(41.67) |
| Normal | 79(28.01) | 60(30.3) | 19(22.62) |
| Not Available | 99(35.11) | 71(35.86) | 28(33.33) |
| White_cell_count_result | |||
| Elevated | 77(27.3) | 56(28.28) | 21(25) |
| Low | 3(1.06) | 1(0.51) | 2(2.38) |
| Normal | 150(53.19) | 100(50.51) | 50(59.52) |
| Not Available | 52(18.44) | 41(20.71) | 11(13.09) |
| Race_list.race | |||
| BLACK OR AFRICAN AMERICAN | 47(16.67) | 34(17.17) | 13(15.48) |
| Not Available | 3(1.06) | 1(0.51) | 2(2.38) |
| WHITE | 232(82.27) | 163(82.32) | 69(82.14) |
DownLoad:
CSV
We selected 942 differentially methylated sites between recurrence and no recurrence cohorts for univariate Cox proportional hazard regression analysis. As a result, 403 DNA methylation sites were proved to be strongly involved in KIRC patients' RFS (P < 0.05) (Table S1). After that the above 403 KIRC DNA methylation sites were projected to LASSO Cox regression model and 18 methylation sites were screened as the candidate prognostic sites (Figure 2) which were significantly related to RFS of KIRC patients. Those 18 candidate methylation sites were further analyzed by multivariable Cox analysis in the training set. Finally, 9 methylation sites (P < 0.05) were identified as the independent risk factors tightly associated with RFS of KIRC patients, including cg12009697, cg14207589, cg07990546, cg04094846, cg11132272, ca11955474, cg03010887, cg09217923, cg25206071. These 9 methylation sites were used for building a DNA methylation-based signature to predict the RFS of KIRC patients. Therefore, we constructed the risk score formula as follows: risk score = 2.625*cg14207589 + 4.519*cg09217923 + 2.615*cg11132272 - 2.373*cg12009697 + 2.751*cg03010887 + 1.568*cg07990546 + 1.344*cg04094846 + 1.507*cg11955474 + 2.065*cg25206071. We discovered that, the hypermethylation levels of cg14207589, cg09217923, cg11132272, cg03010887, cg07990546, cg04094846, cg11955474, cg25206071 tended to have poor survival rates. Whereas the hypomethylation levels of cg12009697 tended to have poor survival rates. (Figures 3 and S1).
In order to analyze the predictive capability of this 9 DNA methylation-based signature, we divided the patients in each set into high and low-risk cohorts based on the median risk score. Kaplan–Meier survival analysis was exploited to examine the difference in RFS between the two cohorts. The patients in high- risk cohort generated a significantly unfavorable RFS in internal validation set (p = 3e-04) (Figure 4A), similar results were obtained in external validation set (p = 8e-04) (Figure 4C) and entire TCGA set (p = 2e-13) (Figure 4E). After that, the power of the 9-DNA methylation signature-based indicator for KIRC patients' RFS was explored by using a time-dependent ROC curve. The AUC of the biomarker at 1, 3, 5 years in internal set were 0.859, 0.840, 0.817, respectively (Figure 4B), in external validation set (0.674, 0.739, 0.793, respectively) (Figure 4D) and entire TCGA set (0.834, 0.862, 0.842, respectively) (Figure 4F). We concluded that the 9-DNA methylation signature-related indicator yielded a great power for the prediction of KIRC patients' RFS.
Subsequently, the rank of the patients from entire TCGA set was carried out according to the risk scores (Figure 5A), and the dotplot of the total patients was performed according to their survival status (Figure 5B). We noted that the high-risk cohort generated a worse survival than that in the low-risk cohort. Figure 5C showed the heatmap of 9 methylation sites distribution according to risk score, which supported the previous observation (Figure S2). Then, we performed subgroup analysis by using a few clinical variables containing age, gender, anatomic site, grade and stage. The majority of sub-groups showed a good predictive power of the 9-DNA methylation signature for KIRC patients' RFS (Figures S3–S7).
We separated patients into high and low-risk groups by using a cutoff risk score of the median risk score. Figure 6A showed the top 20 enriched pathways that were more activated in the high-risk cases than that in low-risk cases. The same trend between the enriched pathways and risk score was further addressed in Figure 6B, which proved a good correlation between the pathways and the risk score (Table S2).
We first performed univariate and multivariate Cox tool according to a few clinic-related covariates. Hazard ratios (HRs) manifested that the 9-DNA methylation-related classifier was tightly related to KIRC patients' RFS (P < 0.001, HR 1.47, 95% CI 1.13–1.92) (Table 2 and Figure S8), manifesting that the 9-DNA methylation signature was an independent predictor of KIRC patients' RFS in the entire TCGA set. To further reinforce the prognostic value of the 9-DNA methylation-based signature for KIRC in a quantitative strategy. We developed a nomogram (Figure 7) that integrated the 9-DNA methylation-associated signature and the conventional clinic-associated covariates containing cancer status, grade. The significance between the 9-DNA methylation-based factor and the conventional clinic-associated covariates was described in Figure 8A. The value of the nomogram was weighed on the basis of C-index (0.802, 95%CI: 0.768-0.828), AUC (1, 3, 5-year: 0.912, 0.922, 0.915) (Figure 8B) and calibration plot (Figure 8C–E), proving a good value of the tool. In addition, DCA manifested that the nomogram created more crucial value of clinical utilization as predictor of KIRC patients' RFS than that in treat all or treat none group. Net benefit was available for KIRC patients in 3-year recurrent risks (Figure 8F). We concluded that our nomogram had a great value and may have potential for clinical application.
| id | HR | HR.95L | HR.95H | pvalue | HR | HR.95L | HR.95H | pvalue |
| Cancer_Status | 6.559682 | 4.597569 | 9.359168 | 3.30E-25 | 4.33397 | 2.931538 | 6.407318 | 1.95E-13 |
| Score | 2.718282 | 2.197252 | 3.362863 | 3.24E-20 | 1.469438 | 1.126449 | 1.916862 | 0.004541 |
| Grade | 2.196578 | 1.686027 | 2.861731 | 5.53E-09 | 1.330598 | 1.025083 | 1.727169 | 0.031865 |
| platelet_qualitative_result | 1.809324 | 1.191503 | 2.747499 | 0.005402 | 1.33846 | 0.878716 | 2.038741 | 0.174536 |
| M | 3.279185 | 2.494814 | 4.310164 | 1.68E-17 | 1.32983 | 0.780402 | 2.266071 | 0.294544 |
| T | 2.776256 | 2.077911 | 3.709301 | 4.94E-12 | 1.327995 | 0.680582 | 2.591271 | 0.405569 |
| Stage | 2.930931 | 2.311473 | 3.7164 | 6.90E-19 | 1.154549 | 0.555477 | 2.399706 | 0.700252 |
| White_cell_count_result | 0.654328 | 0.478444 | 0.894871 | 0.007923 | 0.964921 | 0.695044 | 1.339588 | 0.831069 |
| Serum_calcium_result | 1.477163 | 1.092262 | 1.997699 | 0.011311 | 1.033513 | 0.745413 | 1.432964 | 0.843271 |
| Sex | 1.5336 | 0.9034 | 2.6034 | 0.1132 | ||||
| 23 | 22 | 33 | 39 | |||||
| Laterality | 0.84882 | 0.669924 | 1.07549 | 0.174683 | ||||
| Hemoglobin_result | 0.868924 | 0.667948 | 1.130369 | 0.295156 | ||||
| N | 1.198348 | 0.761475 | 1.885865 | 0.434147 | ||||
| Race | 1.102963 | 0.757493 | 1.605991 | 0.609215 |
DownLoad:
CSV
The KIRC DNA methylation information and relevant clinical information from TCGA database was analyzed to unearth the predictor of KIRC patients' RFS. Eventually, 9 DNA methylation sites (cg12009697, cg14207589, cg07990546, cg04094846, cg11132272, ca11955474, cg03010887, cg09217923, cg25206071) were found to be strongly associated with RFS of KIRC patients. The above 9 DNA methylation sites were mapped to 4 genes (TLX2, OSBP, TAAR2, IL19). It is interesting to note that, previous researches suggested that most of these 4 genes were correlated with cancer, respectively. For instance, Liu et al. found that methylated COL4A1, COL4A2, TLX2, and ITGA4 demonstrated high accuracy for the detection of colorectal neoplasms in stool and they may be potentially valuable biomarkers for the detection of colorectal cancer [27]. Liao et al. demonstrated that TAT-OSBP-MKK6(E) was a novel artificially designed molecule, which induced apoptosis and selectively targeted human ovarian carcinoma HO8910 cells. Their study provided novel insights that may aid in the development of a new generation of anticancer drugs, which implied the importance of OSBP in cancer [28]. Hsing et al. suggested that upregulated IL-19 in breast cancer promoted tumor progression and affected clinical result [29]. The result manifested that the 3 of the 4 genes involved in the 9 sites played important roles in progression of cancer.
Multiple studies indicated that nomograms may strengthen prognostic predictive accuracy for cancer by integrating several clinical variables in a quantitative method. For example, Wang et al. constructed a nomogram to predict gleason sum upgrading of clinically diagnosed localized prostate cancer among Chinese patients [30]. Lee et al. built a prognostic nomogram to predict progression-free survival in patients with platinum-sensitive recurrent ovarian cancer [31]. Taşkın et al. found a nomogram with potential clinical application to predict lymph node metastasis in endometrial cancer patients diagnosed incidentally by postoperative pathological assessment [32]. However, nomograms for improving predictive ability of KIRC patients' RFS have been rarely reported. In this study, we aimed to develop a nomogram using both methylation risk score and several clinic-associated covariates for predicting 1, 3, 5-year RFS for KIRC patient from the entire TCGA set. The indicators of the C-index, AUC, DCA and calibration curve from the entire TCGA set exhibited that our nomogram had discriminative accuracy and was adopted as the preferred predictive model. Furthermore, we developed the nomogram as a predicator in a quantitative method for the prognosis of KIRC patient, which could predict the accruate survival percentage of KIRC patients. In addition, the key virtues of the nomogram contained clinical applicability and convenient factors, which was readily available according to historical records. On the other hand, DNA methylation profiling is highly accurate and reproducible even using small specimens and poor quality material [18], suggesting that this methylomics-correlated nomogram method had a great advantage to predict prognosis of cancer patients in comparison to other signatures.
Growing researches manifested that LASSO Cox regression model can be employed to unearth markers of multiple cancers, For example, Connell et al. found a four-group urine risk classifier for predicting outcome in prostate cancer patients based on LASSO Cox regression analysis [33]. Zhang et al. identified a new eight-long noncoding RNA molecular signature for breast cancer survival prediction according to the LASSO method, univariate and multivariate Cox analyses [34]. Liu et al. identified an eight-lncRNA prognostic model for breast cancer using WGCNA network analysis and a Cox‑proportional hazards model based on LASSO Cox regression model [35]. Du et al. constructed a diagnostic nomogram of platelet-based score models for hepatic alveolar echinococcosis and atypical liver cancer using LASSO Cox regression model [36]. A significant merit of LASSO is that it has sparse estimates of the regression coefficients which suggests that various components are precisely 0. In other words, LASSO can automatically remove unnecessary variables. LASSO has various preferred properties for regression models with numerous variables, and multiple efficient optimization algorithms are available for linear regression and generalized linear models [37]. To our knowledge, fewer studies have used LASSO regression model for the determination of a prognostic predictor of KIRC. We performed LASSO Cox regression analysis to select candidate methylation sites tightly related to KIRC patients' RFS for filtering the variables between univariate and multivariate Cox analysis, further improving prognostic predicted ability of 9-DNA methylation-associated signature.
There are still a few limitations of this study. Firstly, our study is a retrospective one and the prospective studies including a larger number of samples of various medical centers are needed to verify the outcomes. Secondly, more factors should be mapped into our external validation set to improve the predicted accuracy of the nomogram. Thirdly, the nomograms were developed by using retrospective data from TCGA database, which may be correlated with the potential hazard of selection bias.
We successfully identified a 9-DNA methylation-based signature, which was a useful prognostic predictor for KIRC patients' RFS. A nomogram combining methylation risk score and the conventional clinic-related covariates was helpful in improving the predicted ability of clinical prognosis for KIRC patients.
This work was supported by the National Nature Science Foundation of China (Grant Nos. 82073324).
The authors declare that they have no competing interests.
| [1] | C. C. Chow, K. D. Hall, The dynamics of human body weight change, PLOS Comput. Biol., 4 (2008). |
| [2] | WHO obesity and overweight, 2018. Available from: http://www.who.int/mediacentre/factsheets/fs311/en/. |
| [3] | H. Gu, Sh. Shao, J. Liu, Zh. Fan, Y. Chen, J. Ni, et.al., Age- and sex-associated impacts of body mass index on stroke type risk: a 27-year prospective cohort study in a low-income population in china, Front. Neurol., 10 (2019). |
| [4] | K. Cheng, Health oriented lifelong nutrition controls: preventing cardiovascular diseases caused by obesity, SM J. Nutr. Metab., 6 (2020), 1–5. |
| [5] | K. Mc Namara, H. Alzubaidi, J. K. Jackson, Cardiovascular disease as a leading cause of death: how are pharmacists getting involved?, Integr. Pharm. Res. Pract., 8 (2019), 1–11. |
| [6] | E. J. Benjamin, P. Muntner, A. Alonso, M. S. Bittencourt, C. W. Callaway, A. P. Carson, et. al., Heart disease and stroke statistics—2019 update: a report from the american heart association, Circulation, 139 (2019), 56–528 |
| [7] | N. Taghizadeh,, H. M. Boezen, J. P. Schouten, C. P. Schröder, E. G. E. de Vries, J. M. Vonk, BMI and lifetime changes in BMI and cancer mortality risk, PLoS ONE, 10 (2015). |
| [8] | K. Bhaskaran, I. Douglas, H. Forbes, I. dos Santos-Silva, D. A. Leon, and L. Smeeth, Bodymass index and risk of 22 specific cancers: a population-based cohort study of 5.24 million UK adults, Lancet, 384 (2014), 755–765. |
| [9] | A. S. Barnes, The epidemic of obesity and diabetes, Tex. Heart I. J., 38 (2011), 142–144. |
| [10] | A. Golay and J. Ybarra, Link between obesity and type 2 diabetes, Best Pract. Res. Cl. En., 19 (2005), 649–663. |
| [11] | A. S. Al-Goblan, M. A. Al-Alfi, and M. Z. Khan, Mechanism linking diabetes mellitus and obesity, Diabetes Metab. Syndr. Obes., 7 (2014), 587–591. |
| [12] | N. A. Roper, R. W. Bilous, W. F. Kelly, N. C. Unwin, and V. M. Connolly, Cause-specific mortality in a population with diabetes: south tees diabetes mortality study, Diabetes Care, 25 (2002), 43–48. |
| [13] | M. Tancredi, A. Rosengren, A.-M. Svensson, M. Kosiborod, A. Pivodic, S. Gudbjörnsdottir, et. al., Excess mortality among persons with type 2 diabetes, New Engl. J. Med., 373 (2015), 1720–1732. |
| [14] | M. Kalligeros, F. Shehadeh, E. K. Mylona, G. Benitez, C. G. Beckwith, P. A. Chan, et.al., Association of obesity with disease severity among patients with coronavirus disease 2019, Obesity, 28 (2020), 1200–1204. |
| [15] | R. A. DeFronzo, R. C. Bonadonna, E. Ferrannini, Pathogenesis of NIDDM: a balanced overview, Diabetes Care, 15 (1992), 318–368. |
| [16] | E. Archer, C. J. Lavie, and J. O. Hill, The contributions of 'diet', 'genes', and physical activity to the etiology of obesity: contrary evidence and consilience, Prog. Cardiovasc. Dis., 61 (2018), 89–102. |
| [17] | A. D. Baron, G. Brechtel, P. Wallace, S. V. Edelman, Rates and tissue sites of non-insulin- and insulin-mediated glucose uptake in humans, Am. J. Physiol. Endoc. M., 255 (1988), 769–774. |
| [18] | R. A. DeFronzo, D. Tripathy, Skeletal muscle insulin resistance is the primary defect in type 2 diabetes, Diabetes Care, 32 (2009), S157–S163. |
| [19] | E. Archer, G. Pavela, S. McDonald, C. J. Lavie, and J. O. Hill, Cell-specific "competition for calories" drives asymmetric nutrient-energy partitioning, obesity, and metabolic diseases in human and non-human animals, Front. Physiol., 9 (2018), 1053. |
| [20] | R. A. DeFronzo, The triumvirate: cell, muscle, liver: a collusion responsible for NIDDM, Diabetes, 37 (1988), 667–687. |
| [21] | A. V. Greco, G. Mingrone, A. Giancaterini, M. Manco, M. Morroni, S. Cinti, et. al., Insulin resistance in morbid obesity: reversal with intramyocellular fat depletion, Diabetes, 51 (2002), 144–151. |
| [22] | O. T. Hardy, M. P. Czech, S. Corvera, What causes the insulin resistance underlying obesity?, Curr. Opin. Endocrinol., 19 (2012), 81–87. |
| [23] | C. Roberts-Toler, B. T. O'Neill, A. M. Cypess, Diet-induced obesity causes insulin resistance in mouse brown adipose tissue: dio causes bat insulin resistance, Obesity, 23 (2015), 1765–1770. |
| [24] | R. Firth, P. Bell, H. Marsh, I. Hansen, R. Rizza, Postprandial hyperglycemia in patients with noninsulin-dependent diabetes mellitus, J. Clin. Invest., 77 (1986), 1525–1532. |
| [25] | Understanding satiety: feeling full after a meal - British nutrition foundation, 2018. Available from: https://www.nutrition.org.uk/healthyliving/fuller/understanding-satiety-feeling-full-after-a-meal.html. |
| [26] | E. Bilman, E. van Kleef, H. van Trijp, External cues challenging the internal appetite control system—overview and practical implications, Cr. Rev. Food Sci., 57 (2017), 2825–2834. |
| [27] | EO. G. Edholm, J. G. Fletcher, E. M. Widdowson, R. A. McCance, The energy expenditure and food intake of individual men, Brit. J. Nutr., 9 (1955), 286–300. |
| [28] | C. B. Saper, T. C. Chou, J. K. Elmquist, The need to feed: homeostatic and hedonic control of eating, Neuron, 36 (2002), 199–211. |
| [29] | E. Mamontov, Modelling homeorhesis by ordinary differential equations, Math. Comput. Model., 45 (2007), 694–707. |
| [30] | D. F. Marks, Homeostatic theory of obesity, Health Psychology Open, 2 (2015), 1–30. |
| [31] | R. D. Palmiter, Is dopamine a physiologically relevant mediator of feeding behavior?, Trends Neurosci., 30 (2007), 375–381. |
| [32] | D. E. Cummings, Ghrelin and the short- and long-term regulation of appetite and body weight, Physiol. Behav., 89 (2006), 71–84. |
| [33] | J. Vartiainen, Ghrelin, obesity and type 2 diabetes: genetic, metabolic and epidemiological studies, Ph.D thesis, University of Oulu, 2009. |
| [34] | I. Nilsson, C. Lindfors, S. O. Fetissov, T. Hökfelt, J. E. Johansen, Aberrant agouti-related protein system in the hypothalamus of the anx/anx mouse is associated with activation of microglia, J. Comp. Neurol., 507 (2008), 1128–1140. |
| [35] | A. M. Chao, A. M. Jastrebo, M. A. White, C. M. Grilo, R. Sinha, Stress, cortisol, and other appetite-related hormones: prospective prediction of 6-month changes in food cravings and weight, Obesity (Silver Spring, Md.), 25 (2017), 713–720. |
| [36] | A. Uchida, J. M. Zigman, and M. Perello, Ghrelin and eating behavior: evidence and insights from genetically-modified mouse models, Front. Neurosci., 7 (2013), 121. |
| [37] | Y. Sun, S. Ahmed, R. G. Smith, Deletion of ghrelin impairs neither growth nor appetite, Mol. Cell. Biol., 23 (2003), 7973–7981. |
| [38] | K. E. Wortley, K. D. Anderson, K. Garcia, J. D. Murray, L. Malinova, R. Liu, M. Moncrieffe, et.al., Genetic deletion of ghrelin does not decrease food intake but influences metabolic fuel preference, P. Natl. Acad. Sci. USA., 101 (2004), 8227–8232. |
| [39] | K. E. Wortley, J.-P. del Rincon, J. D. Murray, K. Garcia, K. Iida, M. O. Thorner, et. al., Absence of ghrelin protects against early-onset obesity, J. Clin. Invest., 115 (2005), 3573–3578. |
| [40] | B. De Smet, I. Depoortere, D. Moechars, Q. Swennen, B. Moreaux, K. Cryns, et. al., Energy homeostasis and gastric emptying in ghrelin knockout mice, J. Pharmacol. Exp. Ther., 316 (2006), 431–439. |
| [41] | K. Dezaki, H. Sone, M. Koizumi, M. Nakata, M. Kakei, H. Nagai, et. al., Blockade of pancreatic islet-derived ghrelin enhances insulin secretion to prevent high-fat diet-induced glucose intolerance, Diabetes, 55 (2006), 3486–3493. |
| [42] | P. T. Pfluger, H. Kirchner, S. Günnel, B. Schrott, D. Perez-Tilve, S. Fu, et. al., Simultaneous deletion of ghrelin and its receptor increases motor activity and energy expenditure, Am. J. Physiol. Gastrointest. Liver Physiol., 294 (2008), 610–618. |
| [43] | T. Sato, M. Kurokawa, Y. Nakashima, T. Ida, T. Takahashi, Y. Fukue, et. al., Ghrelin deficiency does not influence feeding performance., Regul. Peptides, 145 (2008), 7–11. |
| [44] | Y. Sun, P. Wang, H. Zheng, R. G. Smith, Ghrelin stimulation of growth hormone release and appetite is mediated through the growth hormone secretagogue receptor, P. Natl. Acad. Sci. USA., 101 (2004), 4679–4684. |
| [45] | A. Abizaid, Z.-W. Liu, Z. B. Andrews, M. Shanabrough, E. Borok, J. D. Elsworth, et. al., Ghrelin modulates the activity and synaptic input organization of midbrain dopamine neurons while promoting appetite, J. Clin. Invest., 116 (2006), 3229–3239. |
| [46] | I. D. Blum, Z. Patterson, R. Khazall, E. W. Lamont, M. W. Sleeman, T. L. Horvath, et. al., Reduced anticipatory locomotor responses to scheduled meals in ghrelin receptor deficient mice, Neuroscience, 164 (2009), 351–359. |
| [47] | L. Lin, P. K. Saha, X. Ma, I. O. Henshaw, L. Shao, B. H. J. Chang, E. D. Buras, et. al., Ablation of ghrelin receptor reduces adiposity and improves insulin sensitivity during aging by regulating fat metabolism in white and brown adipose tissues, Aging Cell, 10 (2011), 996–1010. |
| [48] | X. Ma, L. Lin, G. Qin, X. Lu, M. Fiorotto, V. D. Dixit, et. al., Ablations of ghrelin and ghrelin receptor exhibit differential metabolic phenotypes and thermogenic capacity during aging, PLOS ONE, 6 (2011), 1–10. |
| [49] | M. D. Klok, S. Jakobsdottir, M. L. Drent, The role of leptin and ghrelin in the regulation of food intake and body weight in humans: a review, Obes. Rev., 8 (2007), 21–34. |
| [50] | D. P. Figlewicz, S. B. Evans, J. Murphy, M. Hoen, D. G. Baskin, Expression of receptors for insulin and leptin in the ventral tegmental area/substantia nigra (vta/sn) of the rat, Brain Res., 964 (2003), 107–115. |
| [51] | D. P. Figlewicz, P. Szot, M. Chavez, S. C. Woods, R. C. Veith, Intraventricular insulin increases dopamine transporter mrna in rat vta/substantia nigra, Brain Res., 644 (1994), 331–334. |
| [52] | What is non-diabetic hypoglycemia?, 2019. Available from: https://www.webmd.com/diabetes/non-diabetic-hypoglycemia. |
| [53] | Hypoglycemia: signs, risks, causes, and how to raise low blood sugar |everyday health, 2019. Available from: https://www.everydayhealth.com/hypoglycemia/guide/. |
| [54] | Low blood sugar? 8 warning signs if you have diabetes, 2019. Available from: https://health.clevelandclinic.org/low-blood-sugar-8-warning-signs-diabetes/. |
| [55] | Understanding hypoglycemia, 2019. Available from: https://www.diabetesselfmanagement.com/managing-diabetes/blood-glucose-management/understanding-hypoglycemia/. |
| [56] | The effects of low blood sugar on your body, 2019. Available from: https://www.healthline.com/health/low-blood-sugar-effects-on-body. |
| [57] | Hypoglycemia (low blood sugar): causes and treatment, 2019. Available from: https://www.medicalnewstoday.com/articles/166815.php. |
| [58] | Non-diabetic hypoglycemia: symptoms, causes, diagnosis, treatment, 2019. Available from: https://www.webmd.com/diabetes/non-diabetic-hypoglycemia#1. |
| [59] | Polyphagia: the relationship between hunger and diabetes, 2019. Available from: https://www.thediabetescouncil.com/. |
| [60] | J. L. Jameson, L. J. D. Groot, Endocrinology: adult and pediatric, 7th edition, Elsevier Health Sciences, 2015. |
| [61] | J. R. Hupp, M. R. Tucker, E. Ellis, Contemporary oral and maxillofacial surgery, 1st edition, Elsevier Health Sciences, 2013. |
| [62] | D. A. Schatz, M. Haller, M. Atkinson, Type 1 diabetes, an issue of endocrinology and metabolism clinics of north america, 1st edition, Elsevier Health Sciences, 2010. |
| [63] | L. A. Fleisher, M. F. Roizen, J. Roizen, Essence of anesthesia practice, 4th edition, Elsevier Health Sciences, 2017. |
| [64] | G. Cheney, Medical management of gastrointestinal disorders, 1st edition, Year Book, 1950. |
| [65] | M, Manthappa, How to manage your diabetes and lead a normal life, 1st edition, Peacock Books, 2009. |
| [66] | R. K. Bernstein, Hunger–a common symptom of hypoglycemia, Diabetes Care, 16 (1993), 1049. |
| [67] | C. Kenny, When hypoglycemia is not obvious: diagnosing and treating under-recognized and undisclosed hypoglycemia, Prim. Care Diabetes, 8 (2014), 3–11. |
| [68] | J. Morales, D. Schneider, Hypoglycemia, Am. J. Med., 127 (2014), 17–24. |
| [69] | L. C. Perlmuter, B. P. Flanagan, P. H. Shah, S. P. Singh, Glycemic control and hypoglycemia, Diabetes Care, 31 (2008), 2072–2076. |
| [70] | B. Schultes, K. M. Oltmanns, W. Kern, H. L. Fehm, J. Born, A. Peters, Modulation of hunger by plasma glucose and metformin, J. Clin. Endocrinol. Metab., 88 (2003), 1133–1141. |
| [71] | B. Schultes, A. Peters, M. Hallschmid, C. Benedict, V. Merl, K. M. Oltmanns, et. al., Modulation of food intake by glucose in patients with type 2 diabetes, Diabetes Care, 28 (2005), 2884–2889. |
| [72] | H. A. J. Gielkens, M. Verkijk, W. F. Lam, C. B. H. W. Lamers, A. A. M. Masclee, Effects of hyperglycemia and hyperinsulinemia on satiety in humans, Metabolism, 47 (1998), 321–324. |
| [73] | B. Schultes, A. K. Panknin, M. Hallschmid, K. Jauch-Chara, B.Wilms, F. de Courbière, et. al., Glycemic increase induced by intravenous glucose infusion fails to affect hunger, appetite, or satiety following breakfast in healthy men, Appetite, 105 (2016), 562–566. |
| [74] | J. M. McMillin, Blood glucose, 3rd edition, Butterworths, Boston, 1990. |
| [75] | J. Yokrattanasak, A. De Gaetano, S. Panunzi, P. Satiracoo, W. M. Lawton, Y. Lenbury, A simple, realistic stochastic model of gastric emptying, PLoS ONE, 11 (2016). |
| [76] | L. K. Phillips, C. K. Rayner, K. L. Jones, M. Horowitz, Measurement of gastric emptying in diabetes, J. Diabetes Complicat., 28 (2014), 894–903. |
| [77] | S. G. Cao, H. Wu, Z. Z. Cai, Dose-dependent effect of ghrelin on gastric emptying in rats and the related mechanism of action, The Kaohsiung J. Med. Sci., 32 (2016), 113–117. |
| [78] | L. X. Yu, G. L. Amidon, A compartmental absorption and transit model for estimating oral drug absorption, Int. J. Pharm., 186 (1999), 119–125. |
| [79] | K. Ogungbenro, L. Aarons, A semi-mechanistic gastric emptying pharmacokinetic model for (13)C-octanoic acid: an evaluation using simulation, Eur. J. Pharm. Sci., 45 (2012), 302–310. |
| [80] | J. D. Berke, S. E. Hyman, Addiction, dopamine, and the molecular mechanisms of memory, Neuron, 25 (2000), 515–532. |
| [81] | R. A. Wise, Addictive drugs and brain stimulation reward, Annu. Rev. Neurosci., 19 (1996), 319–340. |
| [82] | P. M. Milner, Brain-stimulation reward: a review, Can. J. Psychol., 45 (1991), 1–36. |
| [83] | J. M. Liebman, Discriminating between reward and performance: a critical review of intracranial self-stimulation methodology, Neurosci. Biobehav. R., 7 (1983), 45–72. |
| [84] | R. A. Wise, Brain reward circuitry: insights from unsensed incentives, Neuron, 36 (2002), 229–240. |
| [85] | A. E. Kelley, V. P. Bakshi, S. N. Haber, T. L. Steininger, M. J.Will, M. Zhang, Opioid modulation of taste hedonics within the ventral striatum, Physiol. Behav., 76 (2002), 365–377. |
| [86] | R. Coccurello, M. Maccarrone, Hedonic eating and the "delicious circle": from lipid-derived mediators to brain dopamine and back, Front. Neurosci-Switz., 12 (2018). |
| [87] | S. L. Teegarden, T. L. Bale, Decreases in dietary preference produce increased emotionality and risk for dietary relapse, Biol. Psychiat., 61 (2007), 1021–1029. |
| [88] | B. G. Hoebel, N. M. Avena, M. E. Bocarsly, P. Rada, Natural addiction: a behavioral and circuit model based on sugar addiction in rats, J. Addict. Med., 3 (2009), 33–41. |
| [89] | J.W. Dalley, B. J. Everitt, T.W. Robbins, Impulsivity, compulsivity, and top-down cognitive control, Neuron, 69 (2011), 680–694. |
| [90] | E. N. Pothos, V. Davila, D. Sulzer, Presynaptic recording of quanta from midbrain dopamine neurons and modulation of the quantal size, J. Neurosci., 18 (1998), 4106–4118. |
| [91] | S. Fulton, P. Pissios, R. P. Manchon, L. Stiles, L. Frank, E. N. Pothos, et. al., Leptin regulation of the mesoaccumbens dopamine pathway, Neuron, 51 (2006), 811–822. |
| [92] | K. Toshinai, Y. Date, N. Murakami, M. Shimada, M. S. Mondal, T. Shimbara, J. L. Guan, et. al., Ghrelin-induced food intake is mediated via the orexin pathway, Endocrinology, 144 (2003), 1506–1512. |
| [93] | H. Y. Chen, M. E. Trumbauer, A. S. Chen, D. T. Weingarth, J. R. Adams, E. G. Frazier, et. al., Orexigenic action of peripheral ghrelin is mediated by neuropeptide y and agouti-related protein, Endocrinology, 145 (2004), 2607–2612. |
| [94] | S. Luquet, C. T. Phillips, R. D. Palmiter, NPY/AgRP neurons are not essential for feeding responses to glucoprivation, Peptides, 28 (2007), 214–225. |
| [95] | K. Bugarith, T. T. Dinh, A. J. Li, R. C. Speth, S. Ritter, Basomedial hypothalamic injections of neuropeptide Y conjugated to saporin selectively disrupt hypothalamic controls of food intake, Endocrinology, 146 (2005), 1179–1191. |
| [96] | Y. Date, T. Shimbara, S. Koda, K. Toshinai, T. Ida, N. Murakami, et. al., Peripheral ghrelin transmits orexigenic signals through the noradrenergic pathway from the hindbrain to the hypothalamus, Cell Metab., 4 (2006), 323–331. |
| [97] | M. L. Westwater, P. C. Fletcher, H. Ziauddeen, Sugar addiction: the state of the science, Eur. J. Nutr., 55 (2016), 55–69. |
| [98] | P. C. Fletcher, P. J. Kenny, Food addiction: a valid concept?, Neuropsychopharmacol., 43 (2018), 2506–2513. |
| [99] | T. L. Davidson, S. Jones, M. Roy, R. J. Stevenson, The cognitive control of eating and body weight: it's more than what you "think", Front. Psychol., 10 (2019). |
| [100] | A. De Gaetano, T. A. Hardy, A novel fast-slow model of diabetes progression: insights into mechanisms of response to the interventions in the diabetes prevention program, PLOS ONE, 14 (2019), 1–39. |
| [101] | J. Ha, L. S. Satin, A. S. Sherman, A mathematical model of the pathogenesis, prevention, and reversal of type 2 diabetes, Endocrinology, 157 (2016), 624–635. |
| [102] | A. Borri, S. Panunzi, A. De Gaetano, A glycemia-structured population model, J. Math. Biol., 73 (2016), 39–62. |
| [103] | P. Palumbo, S. Ditlevsen, A. Bertuzzi, A. De Gaetano, Mathematical modeling of the glucose-insulin system: a review, Math. Biosci., 244 (2013), 69–81. |
| [104] | I. Ajmera, M. Swat, C. Laibe, N. L. Nov'ere, V. Chelliah, The impact of mathematical modeling on the understanding of diabetes and related complications, CPT: Pharmacometrics Syst. Pharmacol., 2 (2013). |
| [105] | T. Hardy, E. Abu-Raddad, N. Porksen, A. De Gaetano, Evaluation of a mathematical model of diabetes progression against observations in the diabetes prevention program, Am. J. Physiol. Endoc. M., 303 (2012), 200–212. |
| [106] | J. Ribbing, B. Hamrén, M. K. Svensson, M. O. Karlsson, A model for glucose, insulin, and beta-cell dynamics in subjects with insulin resistance and patients with type 2 diabetes, J. Clin. Pharmacol., 50 (2010), 861–872. |
| [107] | A. De Gaetano, T. Hardy, B. Beck, E. Abu-Raddad, P. Palumbo, J. Bue-Valleskey, et. al., Mathematical models of diabetes progression, Am. J. Physiol. Endoc. M., 295 (2008), 1462–1479. |
| [108] | C. C. Mason, R. L. Hanson, W. C. Knowler, Progression to type 2 diabetes characterized by moderate then rapid glucose increases, Diabetes, 56 (2007), 2054–2061. |
| [109] | W. de Winter, J. DeJongh, T. Post, B. Ploeger, R. Urquhart, I. Moules, et. al., A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying type 2 diabetes mellitus, J. Pharmacokinet. Phar., 33 (2006), 313–343. |
| [110] | A. Bagust, M. Evans, S. Beale, P. D. Home, A. S. Perry, M. Stewart, A model of long-term metabolic progression of type 2 diabetes mellitus for evaluating treatment strategies, PharmacoEconomics, 24 Suppl 1 (2006), 5–19. |
| [111] | B. Topp, K. Promislow, G. deVries, R. M. Miura, D. T. Finegood, A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes, J. Theor. Biol., 206 (2000), 605–619. |
| [112] | T. Okura, R. Nakamura, Y. Fujioka, S. Kawamoto-Kitao, Y. Ito, K. Matsumoto, et. al., Body mass index is a risk factor for insulin resistance and diabetes in japanese people: a brief report, PLOS ONE, 13 (2018). |
| [113] | Y. H. Cheng, Y. C. Tsao, I. S. Tzeng, H. H. Chuang, W. C. Li, T. H. Tung, et. al., Body mass index and waist circumference are better predictors of insulin resistance than total body fat percentage in middle-aged and elderly Taiwanese, Medicine, 96 (2017). |
| [114] | J. A. Hawley, Exercise as a therapeutic intervention for the prevention and treatment of insulin resistance, Diabetes Metab. Res. Rev., 20 (2004), 383–393. |
| [115] | R. N. Bergman, Y. Z. Ider, C. R. Bowden, C. Cobelli, Quantitative estimation of insulin sensitivity., Am. J. Physiol. Endoc. M., 236 (1979). |
| [116] | G. Toffolo, R. N. Bergman, D. T. Finegood, C. R. Bowden, C. Cobelli, Quantitative estimation of beta cell sensitivity to glucose in the intact organism: a minimal model of insulin kinetics in the dog, Diabetes, 29 (1980), 979–990. |
| [117] | C. Dalla Man, R. A. Rizza, C. Cobelli, Meal simulation model of the glucose-insulin system, IEEE trans. Biomed. Eng., 54 (2007), 1740–1749. |
| [118] | W. Liu, F. Tang, Modeling a simplified regulatory system of blood glucose at molecular levels, J. Theor. Biol., 252 (2008), 608–620. |
| [119] | W. Liu, C. Hsin, F. Tang, A molecular mathematical model of glucose mobilization and uptake, Math. Biosci., 221 (2009), 121–129. |
| [120] | Z. Wu, C. K. Chui, G. S. Hong, S. Chang, Physiological analysis on oscillatory behavior of glucose–insulin regulation by model with delays, J. Theor. Biol., 280 (2011), 1–9. |
| [121] | M. Lombarte, M. Lupo, G. Campetelli, M. Basualdo, A. Rigalli, Mathematical model of glucose–insulin homeostasis in healthy rats, Math. Biosci., 245 (2013), 269–277. |
| [122] | A. C. Pratt, J. A. D. Wattis, A. M. Salter, Mathematical modelling of hepatic lipid metabolism, Math. Biosci., 262 (2015), 167–181. |
| [123] | J. Girard, The incretins: from the concept to their use in the treatment of type 2 diabetes. part a: incretins: concept and physiological functions, Diabetes Metab., 34 (2008), 550–559. |
| [124] | J. J. Holst, C. F. Deacon, T. Vilsbøll, T. Krarup, S. Madsbad, Glucagon-like peptide-1, glucose homeostasis and diabetes, Trends Mol. Med., 14 (2008), 161–168. |
| [125] | J. J. Holst, T. Vilsbøll, C. F. Deacon, The incretin system and its role in type 2 diabetes mellitus, Mol. Cell. Endocrinol., 297 (2009), 127–136. |
| [126] | K. Kazakos, Incretin effect: GLP-1, GIP, DPP4, Diabetes Res. Clin. Pr., 93 (2011), 32–36. |
| [127] | J. J. Holst, C. F. Deacon, Is there a place for incretin therapies in obesity and prediabetes?, Trends Endocrin. Met., 24 (2013), 145–152. |
| [128] | S. Masroor, M. G. J. van Dongen, R. Alvarez-Jimenez, K. Burggraaf, L. A. Peletier, M. A. Peletier, Mathematical modeling of the glucagon challenge test, J. Pharmacokinet. Phar., 46 (2019), 553–564. |
| [129] | S. J. Russell, F. H. El-Khatib, M. Sinha, K. L. Magyar, K. McKeon, L. G. Goergen, et. al., Outpatient glycemic control with a bionic pancreas in type 1 diabetes, New Engl. J. Med., 371 (2014), 313–325. |
| [130] | G. Zhao, D. Wirth, I. Schmitz, M. Meyer-Hermann, A mathematical model of the impact of insulin secretion dynamics on selective hepatic insulin resistance, Nat. Commun., 8 (2017), 1–10. |
| [131] | A. De Gaetano, O. Arino, Mathematical modelling of the intravenous glucose tolerance test, J. Math. Biol., 40 (2000), 136–168. |
| [132] | Y. Lenbury, S. Ruktamatakul, S. Amornsamarnkul, Modeling insulin kinetics: responses to a single oral glucose administration or ambulatory-fed conditions, Biosystems., 59 (2001), 15–25. |
| [133] | A. Mukhopadhyay, A. De Gaetano, O. Arino, Modeling the intra-venous glucose tolerance test: a global study for a single-distributed-delay model, Discrete Contin. Dyn. S., 4 (2004), 407. |
| [134] | U. Picchini, A. De Gaetano, S. Panunzi, S. Ditlevsen, G. Mingrone, A mathematical model of the euglycemic hyperinsulinemic clamp, Theor. Biol. Med. Model., 2 (2005), 44. |
| [135] | U. Picchini, S. Ditlevsen, A. De Gaetano, Modeling the euglycemic hyperinsulinemic clamp by stochastic differential equations, J. Math. Biol., 53 (2006), 771–796. |
| [136] | S. Panunzi, P. Palumbo, A. De Gaetano, A discrete single delay model for the intra-venous glucose tolerance test, Theor. Biol. Med. Model., 4 (2007), 35. |
| [137] | D. V. Giang, Y. Lenbury, A. De Gaetano, P. Palumbo, Delay model of glucose–insulin systems: global stability and oscillated solutions conditional on delays, J. Math. Anal. Appl., 343 (2008), 996–1006. |
| [138] | J. Li, M. Wang, A. De Gaetano, P. Palumbo, S. Panunzi, The range of time delay and the global stability of the equilibrium for an IVGTT model, Math. Biosci., 235 (2012), 128–137. |
| [139] | P. Palumbo, P. Pepe, S. Panunzi, A. De Gaetano, Time-delay model-based control of the glucose-insulin system, by means of a state observer., Eur. J. Control, 6 (2012), 591–606. |
| [140] | P. Toghaw, A. Matone, Y. Lenbury, A. De Gaetano, Bariatric surgery and T2DM improvement mechanisms: a mathematical model, Theor Biol Med Model, 9 (2012). |
| [141] | A. De Gaetano, S. Panunzi, A. Matone, A. Samson, J. Vrbikova, B. Bendlova, et. al., Routine OGTT: a robust model including incretin effect for precise identification of insulin sensitivity and secretion in a single individual, PLOS ONE, 8 (2013). |
| [142] | P. Palumbo, G. Pizzichelli, S. Panunzi, P. Pepe, A. De Gaetano, Model-based control of plasma glycemia: tests on populations of virtual patients, Math Biosci, 257 (2014), 2–10. |
| [143] | K. Juagwon, Y. Lenbury, A. De Gaetano, P. Palumbo, Application of modified watanabe's approach for reconstruction of insulin secretion rate during OGTT under non-constant fraction of hepatic insulin extraction, Int. J. Math. Comp. Simul., 7 (2013), 304–313. |
| [144] | A. De Gaetano, S. Panunzi, D. Eliopoulos, T. Hardy, G. Mingrone, Mathematical modeling of renal tubular glucose absorption after glucose load, PLOS ONE, 9 (2014). |
| [145] | S. Sakulrang, E. J. Moore, S. Sungnul, A. De Gaetano, A fractional differential equation model for continuous glucose monitoring data, Adv. Differ. Equ-NY., (2017). |
| [146] | P. Palumbo, A. De Gaetano, An islet population model of the endocrine pancreas, J. Math. Biol., 61 (2010), 171–205. |
| [147] | A. De Gaetano, C. Gaz, P. Palumbo, S. Panunzi, A unifying organ model of pancreatic insulin secretion, PLOS ONE, 10 (2015). |
| [148] | A. De Gaetano, C. Gaz, S. Panunzi, Consistency of compact and extended models of glucose-insulin homeostasis: the role of variable pancreatic reserve, PLOS ONE, 14 (2019). |
| [149] | T. Hardy, E. Abu-Raddad, N. Porksen, A. De Gaetano, Evaluation of a mathematical model of diabetes progression against observations in the diabetes prevention program, Am. J. Physiol. Endocrinol. Metab., 303 (2012), 200–212. |
| [150] | A. Hinsberger, B. K. Sandhu, Digestion and absorption, Current Paediatrics, 14 (2004), 605–611. |
| [151] | A. D. Jackson, J. McLaughlin, Digestion and absorption, Surgery, 27 (2009), 231–236. |
| [152] | I. Campbell, Digestion and absorption, Anaesth. Intens. Care Med., 13 (2012), 62–63. |
| [153] | P. R. Kiela, F. K. Ghishan, Physiology of intestinal absorption and secretion, Best Practice & Research Clinical Gastroenterology, 30 (2016), 145–159. |
| [154] | A. B. Strathe, A. Danfær, A. Chwalibog, A dynamic model of digestion and absorption in pigs, Anim. Feed Sci. Tech., 143 (2008), 328–371. |
| [155] | S. Salinari, A. Bertuzzi, G. Mingrone, Intestinal transit of a glucose bolus and incretin kinetics: a mathematical model with application to the oral glucose tolerance test, Am. J. Physiol. Endoc. M., 300 (2011), 955–965. |
| [156] | M. Taghipoor, G. Barles, C. Georgelin, J. R. Licois, P. Lescoat, Digestion modeling in the small intestine: impact of dietary fiber, Math. Biosci., 258 (2014), 101–112. |
| [157] | E. D. Lehmann, T. Deutsch, , A physiological model of glucose-insulin interaction in type 1 diabetes mellitus, J. Biomed. Eng., 14 (1992), 235–242. |
| [158] | A. Roy, R. S. Parker, Dynamic modeling of exercise effects on plasma glucose and insulin levels, J. Diabetes Sci. Tech., 1 (2007), 338–347. |
| [159] | R. Hovorka, V. Canonico, L. J. Chassin, U. Haueter, M. Massi-Benedetti, M. O. Federici, et. al., Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes, Physiol. Meas., 25 (2004), 905–920. |
| [160] | G. M. Barnwell, F. S. Stafford, Mathematical model for decision-making neural circuits controlling food intake, B. Psychonomic Soc., 5 (1975), 473–476. |
| [161] | R. C. Boston, P. J. Moate, K. C. Allison, J. D. Lundgren, A. J. Stunkard, Modeling circadian rhythms of food intake by means of parametric deconvolution: results from studies of the night eating syndrome, Am. J. Clin. Nutr., 87 (2008), 1672–1677. |
| [162] | F. Cameron, G. Niemeyer, B. A. Buckingham, Probabilistic evolving meal detection and estimation of meal total glucose appearance, J. Diabetes Sci. Tech., 3 (2009), 1022–1030. |
| [163] | N. P. Balakrishnan, L. Samavedham, G. P. Rangaiah, Personalized mechanistic models for exercise, meal and insulin interventions in children and adolescents with type 1 diabetes, J. Theor. Biol., 357 (2014), 62–73. |
| [164] | M. Jacquier, F. Crauste, C. O. Soulage, H. A. Soula, A predictive model of the dynamics of body weight and food intake in rats submitted to caloric restrictions, PLOS ONE, 9 (2014). |
| [165] | A. L. Murillo, M. Safan, C. Castillo-Chavez, E. D. C. Phillips, D. Wadhera, Modeling eating behaviors: the role of environment and positive food association learning via a ratatouille effect, Math. Biosci. Eng., 13 (2016), 841–855. |
| [166] | NHANES 2015-2016 dietary data, 2019. Available from: https://wwwn.cdc.gov/Nchs/Nhanes/Search/DataPage.aspx?Component=Dietary&CycleBeginYear=201. |
| [167] | D. E. Cummings, J. Q. Purnell, R. S. Frayo, K. Schmidova, B. E. Wisse, D. S. Weigle, A preprandial rise in plasma ghrelin levels suggests a role in meal initiation in humans, Diabetes, 50 (2001), 1714–1719. |
| [168] | P. Toghaw, A. Matone, Y. Lenbury, A. De Gaetano, Bariatric surgery and T2DM improvement mechanisms: a mathematical model, Theor. Biol. Med. Model., 9 (2012). |
| [169] | L. Beaugerie, B. Flourié, P. Marteau, P. Pellier, C. Franchisseur, J. C. Rambaud, Digestion and absorption in the human intestine of three sugar alcohols, Gastroenterology, 99 (1990), 717–723. |
| [170] | Y. Tsuchida, S. Hata, Y. Sone, Effects of a late supper on digestion and the absorption of dietary carbohydrates in the following morning, J. Physiol. Anthropol., 32 (2013). |
| [171] | R. M. Atkinson, B. J. Parsons, D. H. Smyth, The intestinal absorption of glucose, J. Physiol., 135 (1957), 581–589. |
| [172] | Big mac®: calories and nutrition |mcdonald's, 2019. Available from: https://www.mcdonalds.com/us/en-us/product/big-mac.htm. |
| [173] | The nutritional content of beer, 2018. Available from: http://www.dummies.com/food-drink/drinks/beer/ the-nutritional-content-of-beer/. |
| [174] | T. M. S. Wolever, Carbohydrate and the regulation of blood glucose and metabolism, Nutr. Rev., 61 (2003), 40–48. |
| [175] | P. J. Randle, P. B. Garland, C. N. Hales, E. A. Newsholme, The glucose fatty-acid cycle. its role in insulin sensitivity and the metabolic disturbances of diabetes mellitus, Lancet, 1 (1963), 785–789. |
| [176] | S. C.Walpole, D. Prieto-Merino, P. Edwards, J. Cleland, G. Stevens, I. Roberts, The weight of nations: an estimation of adult human biomass, BMC Public Health, 12 (2012), 439. |
| [177] | M. E. Clegg, M. Pratt, O. Markey, A. Shafat, C. J. K. Henry, Addition of different fats to a carbohydrate food: impact on gastric emptying, glycaemic and satiety responses and comparison with in vitro digestion, Food Res. Int., 48 (2012), 91–97. |
| [178] | J. G. Moore, P. E. Christian, J. A. Brown, C. Brophy, F. Datz, A. Taylor, et. al., Influence of meal weight and caloric content on gastric emptying of meals in man, Digest. Dis. Sci., 29 (1984), 513–519. |
| [179] | L. Achour, S. Méance, A. Briend, Comparison of gastric emptying of a solid and a liquid nutritional rehabilitation food, Eur. J. Clin. Nutr., 55 (2001), 769–772. |
| [180] | I. Locatelli, A. Mrhar, M. Bogataj, Gastric emptying of pellets under fasting conditions: a mathematical model, Pharm. Res., 26 (2009), 1607–1617. |
| [181] | Fasting blood sugar levels, 2017. Available from: https://www.diabetes.co.uk. |
| [182] | R Core Team, R: a language and environment for statistical computing, 1st edition, Vienna, Austria, 2019. |
| [183] | M. Macht, G. Simon, Emotions and eating in everyday life, Appetite, 35 (2000), 65–71. |
| [184] | L. Canetti, E. Bachar, E. M. Berry, Food and emotion, Behav. Process., 60 (2002), 157–164. |
| [185] | S. Peciña, K. S. Smith, Hedonic and motivational roles of opioids in food reward: implications for overeating disorders, Pharmacol. Biochem. Behav., 97 (2010), 34–46. |
| [186] | S. Fulton, Appetite and reward, Front. Neuroendocrin., 31 (2010), 85–103. |
| [187] | A. H. Sam, R. C. Troke, T. M. Tan, G. A. Bewick, The role of the gut/brain axis in modulating food intake, Neuropharmacology, 63 (2012), 46–56. |
| [188] | E. L. Gibson, Emotional influences on food choice: sensory, physiological and psychological pathways, Physiol. Behav., 89 (2006), 53–61. |
| [189] | D. A. Zellner, S. Loaiza, Z. Gonzalez, J. Pita, J. Morales, D. Pecora, et. al., Food selection changes under stress, Physiol. Behav., 87 (2006), 789–793. |
| [190] | P. M. A. Desmet, H. N. J. Schifferstein, Sources of positive and negative emotions in food experience, Appetite, 50 (2008), 290–301. |
| [191] | M. Macht, How emotions affect eating: a five-way model, Appetite, 50 (2008), 1–11. |
| [192] | L. E. Stoeckel, R. E. Weller, E. W. Cook, D. B. Twieg, R. C. Knowlton, J. E. Cox, Widespread reward-system activation in obese women in response to pictures of high-calorie foods, NeuroImage, 41 (2008), 636–647. |
| [193] | E. Näslund, P. M. Hellström, Appetite signaling: from gut peptides and enteric nerves to brain, Physiol. Behav., 92 (2007), 256–262. |
| [194] | L. Brondel, M. Romer, V. Van Wymelbeke, N. Pineau, T. Jiang, C. Hanus, et. al., Variety enhances food intake in humans: role of sensory-specific satiety, Physiol. Behav., 97 (2009), 44–51. |
| [195] | R. C. Havermans, N. Siep, A. Jansen, Sensory-specific satiety is impervious to the tasting of other foods with its assessment, Appetite, 55 (2010), 196–200. |
| [196] | G. Finlayson, A. Arlotti, M. Dalton, N. King, J. E. Blundell, Implicit wanting and explicit liking are markers for trait binge eating. a susceptible phenotype for overeating, Appetite, 57 (2011), 722–728. |
| [197] | R. J. Stevenson, M. Mahmut, K. Rooney, Individual differences in the interoceptive states of hunger, fullness and thirst, Appetite, 95 (2015), 44–57. |
| [198] | H. Wang, J. Li, Y. kuang, Mathematical modeling and qualitative analysis of insulin therapies, Math. Biosci., 210 (2007), 17–33. |
| [199] | D. M. Thomas, A. Ciesla, J. A. Levine, J. G. Stevens, C. K. Martin, A mathematical model of weight change with adaptation, Math. Biosci. Eng., 6 (2009), 873–887. |
| [200] | C. L. Chen, H. W. Tsai, Modeling the physiological glucose–insulin system on normal and diabetic subjects, Comput. Meth. Prog. Bio., 97 (2010), 130–140. |
| [201] | C. C. Y. Noguchi, E. Furutani, S. Sumi, Enhanced mathematical model of postprandial glycemic excursion in diabetics using rapid-acting insulin, 2012 Proceedings of SICE Annual Conference (SICE), Akita, (2012), 566–571. |
| [202] | H. Zheng, H. R. Berthoud, Eating for pleasure or calories, Curr. Opin. Pharmacol., 7 (2007), 607–612. |
| [203] | O. B. Chaudhri, C. J. Small, S. R. Bloom, The gastrointestinal tract and the regulation of appetite, Drug Discov. Today, 2 (2005), 289–294. |
| [204] | B. M. McGowan, S. R. Bloom, Gut hormones regulating appetite and metabolism, Drug Discov. Today, 4 (2007), 147–151. |
| [205] | B. Meister, Neurotransmitters in key neurons of the hypothalamus that regulate feeding behavior and body weight, Physiol. Behav., 92 (2007), 263–271. |
| [206] | S. Higgs, J. Thomas, Social influences on eating, Curr. Opin. Behav. Sci., 9 (2016), 1–6. |
| [207] | S. Griffioen-Roose, G. Finlayson, M. Mars, J. E. Blundell, C. de Graaf, Measuring food reward and the transfer effect of sensory specific satiety, Appetite, 55 (2010), 648–655. |
| [208] | K. C. Berridge, T. E. Robinson, J. W. Aldridge, Dissecting components of reward: 'liking', 'wanting', and learning, Curr. Opin. Pharmacol., 9 (2009), 65–73. |
| [209] | K. C. Berridge, 'liking' and 'wanting' food rewards: brain substrates and roles in eating disorders, Physiol. Behav., 97 (2009), 537–550. |
| [210] | K. C. Berridge, C. Y. Ho, J. M. Richard, A. G. DiFeliceantonio, The tempted brain eats: pleasure and desire circuits in obesity and eating disorders, Brain Res., 1350 (2010), 43–64. |
| [211] | R. C. Havermans, "You say it's liking, i say it's wanting …". on the difficulty of disentangling food reward in man, Appetite, 57 (2011), 286–294. |
| [212] | R. C. Havermans, How to tell where 'liking' ends and 'wanting' begins, Appetite, 58 (2012), 252–255. |
| [213] | G. Finlayson, M. Dalton, Current progress in the assessment of 'liking' vs. 'wanting' food in human appetite. comment on "you say it's liking, i say it's wanting...". on the difficulty of disentangling food reward in man, Appetite, 58 (2012), 373–378; 252–255. |
| [214] | P. W. J. Maljaars, H. P. F. Peters, D. J. Mela, A. a. M. Masclee, Ileal brake: a sensible food target for appetite control. a review, Physiol. Behav., 95 (2008), 271–281. |
| [215] | H. S. Shin, J. R. Ingram, A. T. McGill, S. D. Poppitt, Lipids, CHOs, proteins: can all macronutrients put a 'brake' on eating?, Physiol. Behav., 120 (2013), 114–123. |
| [216] | A. M. Wren, L. J. Seal, M. A. Cohen, A. E. Brynes, G. S. Frost, K. G. Murphy, et. al., Ghrelin enhances appetite and increases food intake in humans, J. Clin. Endocrinol. Metab., 86 (2001), 5992. |
| [217] | K. A. Levin, Study design III: cross-sectional studies, Evid. Based Dent., 7 (2006), 24–25. |
| [218] | J. Tack, K. J. Lee, Pathophysiology and treatment of functional dyspepsia, J. Clin. Gastroenterol., 39 (2005), 211–216. |
| [219] | S. A. Murray, M. Kendall, K. Boyd, A. Sheikh, Illness trajectories and palliative care, BMJ, 330 (2005), 1007–1011. |
| [220] | M. Binn, C. Albert, A. Gougeon, H. Maerki, B. Coulie, M. Lemoyne, et. al., Ghrelin gastrokinetic action in patients with neurogenic gastroparesis, Peptides, 27 (2006), 1603–1606. |
| [221] | A. Abizaid, T. L. Horvath, Brain circuits regulating energy homeostasis, Regul. Peptides, 149 (2008), 3–10. |
| [222] | M. Traebert, T. Riediger, S. Whitebread, E. Scharrer, H. A. Schmid, Ghrelin acts on leptin-responsive neurones in the rat arcuate nucleus, J. Neuroendocrinol., 14 (2002), 580–586. |
| [223] | Y. C. L. Tung, A. K. Hewson, S. L. Dickson, Actions of leptin on growth hormone secretagogue-responsive neurones in the rat hypothalamic arcuate nucleus recorded in vitro, J. Neuroendocrinol., 13 (2001), 209–215. |
| [224] | N. Sáinz, J. Barrenetxe, M. J. Moreno-Aliaga, J. A. Martínez, Leptin resistance and diet-induced obesity: central and peripheral actions of leptin, Metabolism, 64 (2015), 35–46. |
| [225] | NHANES - participants - why I was selected, 2019. Available from: https://www.cdc.gov/nchs/nhanes/participant/participant-selected.htm. |
| [226] | E. Archer, G. A. Hand, S. N. Blair, Validity of U.S. nutritional surveillance: national health and nutrition examination survey caloric energy intake data, 1971–2010, PLoS ONE, 8 (2013). |
| [227] | E. Archer, G. Pavela, C. J. Lavie, The inadmissibility of what we eat in America and NHANES dietary data in nutrition and obesity research and the scientific formulation of national dietary guidelines, Mayo Clin. Proc., 90 (2015), 911–926. |
| [228] | E. Archer, C. J. Lavie, J. O. Hill, The failure to measure dietary intake engendered a fictional discourse on diet-disease relations, Front. Nutr., 5 (2018), 105. |
| 1. | Alana C. Jones, Amit Patki, Vinodh Srinivasasainagendra, Bertha A. Hidalgo, Hemant K. Tiwari, Nita A. Limdi, Nicole D. Armstrong, Ninad S. Chaudhary, Bré Minniefield, Devin Absher, Donna K. Arnett, Leslie A. Lange, Ethan M. Lange, Bessie A. Young, Clarissa J. Diamantidis, Stephen S. Rich, Josyf C. Mychaleckyj, Jerome I. Rotter, Kent D. Taylor, Holly J. Kramer, Russell P. Tracy, Peter Durda, Silva Kasela, Tuuli Lappalinen, Yongmei Liu, W. Craig Johnson, David J. Van Den Berg, Nora Franceschini, Simin Liu, Charles P. Mouton, Parveen Bhatti, Steve Horvath, Eric A. Whitsel, Marguerite R. Irvin, A methylation risk score for chronic kidney disease: a HyperGEN study, 2024, 14, 2045-2322, 10.1038/s41598-024-68470-z | |
| 2. | Jun Wang, Xiaoming He, Yuanyuan Mi, Yong Q. Chen, Jie Li, Rong Wang, PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma, 2024, 24, 1471-2407, 10.1186/s12885-024-12183-z | |
| 3. | Kathleen C. Barnes, 2025, 9780323988087, 1, 10.1016/B978-0-323-98808-7.00006-0 |
Figures(9) / Tables(2)
Mantana Chudtong, Andrea De Gaetano. A mathematical model of food intake[J]. Mathematical Biosciences and Engineering, 2021, 18(2): 1238-1279. doi: 10.3934/mbe.2021067
| Characteristics | Total (n=282) | Training dataset (n=198) | Testing dataset (n=84) |
| Gender | |||
| FEMALE | 104(36.88) | 73(36.87) | 31(36.9) |
| MALE | 178(63.12) | 125(63.13) | 53(63.1) |
| Age | |||
| ≤ 60 | 132(46.81) | 94(47.47) | 38(45.24) |
| > 60 | 150(53.19) | 104(52.53) | 46(54.76) |
| Stage | |||
| Stage I | 147(52.13) | 101(51.01) | 46(54.76) |
| Stage II | 29(10.28) | 19(9.6) | 10(11.9) |
| Stage III | 68(24.11) | 53(26.77) | 15(17.86) |
| Stage IV | 36(12.77) | 23(11.62) | 13(15.48) |
| Not Available | 2(0.71) | 2(1.01) | |
| M | |||
| M0 | 238(84.4) | 168(84.85) | 70(83.33) |
| M1 | 34(12.06) | 22(11.11) | 12(14.29) |
| Not Available | 10(3.55) | 8(4.04) | 2(2.38) |
| T | |||
| T1 | 149(52.84) | 101(51.01) | 48(57.14) |
| T2 | 37(13.12) | 24(12.12) | 13(15.47) |
| T3 | 93(32.98) | 71(35.86) | 22(26.19) |
| T4 | 3(1.06) | 2(1.01) | 1(1.19) |
| N | |||
| N0 | 119(42.2) | 85(42.93) | 34(40.48) |
| N1 | 8(2.84) | 6(3.03) | 2(2.38) |
| Not Available | 155(54.96) | 107(54.04) | 48(57.14) |
| Grade | |||
| G1 | 8(2.84) | 7(3.54) | 1(1.19) |
| G2 | 122(43.26) | 85(42.93) | 37(44.05) |
| G3 | 110(39.01) | 80(40.4) | 30(35.71) |
| G4 | 38(13.48) | 23(11.62) | 15(17.86) |
| Not Available | 4(1.42) | 3(1.52) | 1(1.19) |
| Cancer_Status | |||
| TUMOR FREE | 201(71.28) | 144(72.73) | 57(67.86) |
| WITH TUMOR | 59(20.92) | 38(19.19) | 21(25) |
| Not Available | 22(7.80) | 16(8.08) | 6(7.14) |
| Laterality | |||
| Bilateral | 1(0.35) | 1(0.51) | |
| Left | 130(46.1) | 92(46.46) | 38(45.24) |
| Right | 151(53.55) | 105(53.03) | 46(54.76) |
| Hemoglobin_result | |||
| Elevated | 4(1.42) | 2(1.01) | 2(2.38) |
| Low | 132(46.81) | 86(43.43) | 46(54.76) |
| Normal | 102(36.17) | 75(37.88) | 27(32.14) |
| Not Available | 44(16.60) | 35(17.68) | 9(10.71) |
| Platelet_qualitative_result | |||
| Elevated | 14(4.96) | 10(5.05) | 4(4.76) |
| Low | 30(10.64) | 24(12.12) | 6(7.14) |
| Normal | 189(67.02) | 125(63.13) | 64(76.19) |
| Not Available | 49(17.38) | 39(19.7) | 10(11.9) |
| Serum_calcium_result | |||
| Elevated | 4(1.42) | 2(1.01) | 2(2.38) |
| Low | 100(35.46) | 65(32.83) | 35(41.67) |
| Normal | 79(28.01) | 60(30.3) | 19(22.62) |
| Not Available | 99(35.11) | 71(35.86) | 28(33.33) |
| White_cell_count_result | |||
| Elevated | 77(27.3) | 56(28.28) | 21(25) |
| Low | 3(1.06) | 1(0.51) | 2(2.38) |
| Normal | 150(53.19) | 100(50.51) | 50(59.52) |
| Not Available | 52(18.44) | 41(20.71) | 11(13.09) |
| Race_list.race | |||
| BLACK OR AFRICAN AMERICAN | 47(16.67) | 34(17.17) | 13(15.48) |
| Not Available | 3(1.06) | 1(0.51) | 2(2.38) |
| WHITE | 232(82.27) | 163(82.32) | 69(82.14) |
DownLoad:
CSV
| id | HR | HR.95L | HR.95H | pvalue | HR | HR.95L | HR.95H | pvalue |
| Cancer_Status | 6.559682 | 4.597569 | 9.359168 | 3.30E-25 | 4.33397 | 2.931538 | 6.407318 | 1.95E-13 |
| Score | 2.718282 | 2.197252 | 3.362863 | 3.24E-20 | 1.469438 | 1.126449 | 1.916862 | 0.004541 |
| Grade | 2.196578 | 1.686027 | 2.861731 | 5.53E-09 | 1.330598 | 1.025083 | 1.727169 | 0.031865 |
| platelet_qualitative_result | 1.809324 | 1.191503 | 2.747499 | 0.005402 | 1.33846 | 0.878716 | 2.038741 | 0.174536 |
| M | 3.279185 | 2.494814 | 4.310164 | 1.68E-17 | 1.32983 | 0.780402 | 2.266071 | 0.294544 |
| T | 2.776256 | 2.077911 | 3.709301 | 4.94E-12 | 1.327995 | 0.680582 | 2.591271 | 0.405569 |
| Stage | 2.930931 | 2.311473 | 3.7164 | 6.90E-19 | 1.154549 | 0.555477 | 2.399706 | 0.700252 |
| White_cell_count_result | 0.654328 | 0.478444 | 0.894871 | 0.007923 | 0.964921 | 0.695044 | 1.339588 | 0.831069 |
| Serum_calcium_result | 1.477163 | 1.092262 | 1.997699 | 0.011311 | 1.033513 | 0.745413 | 1.432964 | 0.843271 |
| Sex | 1.5336 | 0.9034 | 2.6034 | 0.1132 | ||||
| 23 | 22 | 33 | 39 | |||||
| Laterality | 0.84882 | 0.669924 | 1.07549 | 0.174683 | ||||
| Hemoglobin_result | 0.868924 | 0.667948 | 1.130369 | 0.295156 | ||||
| N | 1.198348 | 0.761475 | 1.885865 | 0.434147 | ||||
| Race | 1.102963 | 0.757493 | 1.605991 | 0.609215 |
DownLoad:
CSV
| Characteristics | Total (n=282) | Training dataset (n=198) | Testing dataset (n=84) |
| Gender | |||
| FEMALE | 104(36.88) | 73(36.87) | 31(36.9) |
| MALE | 178(63.12) | 125(63.13) | 53(63.1) |
| Age | |||
| ≤ 60 | 132(46.81) | 94(47.47) | 38(45.24) |
| > 60 | 150(53.19) | 104(52.53) | 46(54.76) |
| Stage | |||
| Stage I | 147(52.13) | 101(51.01) | 46(54.76) |
| Stage II | 29(10.28) | 19(9.6) | 10(11.9) |
| Stage III | 68(24.11) | 53(26.77) | 15(17.86) |
| Stage IV | 36(12.77) | 23(11.62) | 13(15.48) |
| Not Available | 2(0.71) | 2(1.01) | |
| M | |||
| M0 | 238(84.4) | 168(84.85) | 70(83.33) |
| M1 | 34(12.06) | 22(11.11) | 12(14.29) |
| Not Available | 10(3.55) | 8(4.04) | 2(2.38) |
| T | |||
| T1 | 149(52.84) | 101(51.01) | 48(57.14) |
| T2 | 37(13.12) | 24(12.12) | 13(15.47) |
| T3 | 93(32.98) | 71(35.86) | 22(26.19) |
| T4 | 3(1.06) | 2(1.01) | 1(1.19) |
| N | |||
| N0 | 119(42.2) | 85(42.93) | 34(40.48) |
| N1 | 8(2.84) | 6(3.03) | 2(2.38) |
| Not Available | 155(54.96) | 107(54.04) | 48(57.14) |
| Grade | |||
| G1 | 8(2.84) | 7(3.54) | 1(1.19) |
| G2 | 122(43.26) | 85(42.93) | 37(44.05) |
| G3 | 110(39.01) | 80(40.4) | 30(35.71) |
| G4 | 38(13.48) | 23(11.62) | 15(17.86) |
| Not Available | 4(1.42) | 3(1.52) | 1(1.19) |
| Cancer_Status | |||
| TUMOR FREE | 201(71.28) | 144(72.73) | 57(67.86) |
| WITH TUMOR | 59(20.92) | 38(19.19) | 21(25) |
| Not Available | 22(7.80) | 16(8.08) | 6(7.14) |
| Laterality | |||
| Bilateral | 1(0.35) | 1(0.51) | |
| Left | 130(46.1) | 92(46.46) | 38(45.24) |
| Right | 151(53.55) | 105(53.03) | 46(54.76) |
| Hemoglobin_result | |||
| Elevated | 4(1.42) | 2(1.01) | 2(2.38) |
| Low | 132(46.81) | 86(43.43) | 46(54.76) |
| Normal | 102(36.17) | 75(37.88) | 27(32.14) |
| Not Available | 44(16.60) | 35(17.68) | 9(10.71) |
| Platelet_qualitative_result | |||
| Elevated | 14(4.96) | 10(5.05) | 4(4.76) |
| Low | 30(10.64) | 24(12.12) | 6(7.14) |
| Normal | 189(67.02) | 125(63.13) | 64(76.19) |
| Not Available | 49(17.38) | 39(19.7) | 10(11.9) |
| Serum_calcium_result | |||
| Elevated | 4(1.42) | 2(1.01) | 2(2.38) |
| Low | 100(35.46) | 65(32.83) | 35(41.67) |
| Normal | 79(28.01) | 60(30.3) | 19(22.62) |
| Not Available | 99(35.11) | 71(35.86) | 28(33.33) |
| White_cell_count_result | |||
| Elevated | 77(27.3) | 56(28.28) | 21(25) |
| Low | 3(1.06) | 1(0.51) | 2(2.38) |
| Normal | 150(53.19) | 100(50.51) | 50(59.52) |
| Not Available | 52(18.44) | 41(20.71) | 11(13.09) |
| Race_list.race | |||
| BLACK OR AFRICAN AMERICAN | 47(16.67) | 34(17.17) | 13(15.48) |
| Not Available | 3(1.06) | 1(0.51) | 2(2.38) |
| WHITE | 232(82.27) | 163(82.32) | 69(82.14) |
| id | HR | HR.95L | HR.95H | pvalue | HR | HR.95L | HR.95H | pvalue |
| Cancer_Status | 6.559682 | 4.597569 | 9.359168 | 3.30E-25 | 4.33397 | 2.931538 | 6.407318 | 1.95E-13 |
| Score | 2.718282 | 2.197252 | 3.362863 | 3.24E-20 | 1.469438 | 1.126449 | 1.916862 | 0.004541 |
| Grade | 2.196578 | 1.686027 | 2.861731 | 5.53E-09 | 1.330598 | 1.025083 | 1.727169 | 0.031865 |
| platelet_qualitative_result | 1.809324 | 1.191503 | 2.747499 | 0.005402 | 1.33846 | 0.878716 | 2.038741 | 0.174536 |
| M | 3.279185 | 2.494814 | 4.310164 | 1.68E-17 | 1.32983 | 0.780402 | 2.266071 | 0.294544 |
| T | 2.776256 | 2.077911 | 3.709301 | 4.94E-12 | 1.327995 | 0.680582 | 2.591271 | 0.405569 |
| Stage | 2.930931 | 2.311473 | 3.7164 | 6.90E-19 | 1.154549 | 0.555477 | 2.399706 | 0.700252 |
| White_cell_count_result | 0.654328 | 0.478444 | 0.894871 | 0.007923 | 0.964921 | 0.695044 | 1.339588 | 0.831069 |
| Serum_calcium_result | 1.477163 | 1.092262 | 1.997699 | 0.011311 | 1.033513 | 0.745413 | 1.432964 | 0.843271 |
| Sex | 1.5336 | 0.9034 | 2.6034 | 0.1132 | ||||
| 23 | 22 | 33 | 39 | |||||
| Laterality | 0.84882 | 0.669924 | 1.07549 | 0.174683 | ||||
| Hemoglobin_result | 0.868924 | 0.667948 | 1.130369 | 0.295156 | ||||
| N | 1.198348 | 0.761475 | 1.885865 | 0.434147 | ||||
| Race | 1.102963 | 0.757493 | 1.605991 | 0.609215 |
