Komagataeibacter have attracted wide attention due to their particular ability in furthering BC production. Our previous study reported a new strain of genus Komagataeibacter from a vinegar factory. To evaluate its capacity for BC production from different carbon sources, the present study subjected the strain to media spiked with 2% acetate, ethanol, fructose, glucose, lactose, mannitol or sucrose. Then the BC productivity, BC characteristics and biochemical transformation pathways of various carbon sources were fully investigated. After 14 days of incubation, strain W1 produced 0.040–1.529 g L−1 BC, the highest yield being observed in fructose. Unlike BC yields, the morphology and microfibrils of BCs from different carbon sources were similar, with an average diameter of 35–50 nm. X-ray diffraction analysis showed that all membranes produced from various carbon sources had 1–3 typical diffraction peaks, and the highest crystallinity  was found for BC produced from mannitol. Similarly, several typical spectra bands obtained by Fourier transform infrared spectroscopy were similar for the BCs produced from different carbon sources, as was the αI fraction. The genome annotation and Kyoto Encyclopedia of Genes and Genomes analysis revealed that the biochemical transformation pathways associated with the utilization of and BC production from fructose, glucose, glycerol, and mannitol were found in strain W1, but this was not the case for other carbon sources. Our data provides suggestions for further investigations of strain W1 to produce BC by using low molecular weight sugars and gives clues to understand how this strain produces BC based on metabolic pathway analysis.Cellulose is the most abundant and widely used biopolymer on earth and can be produced by both plants and micro-organisms. Among bacterial cellulose (BC)-producing bacteria, the strains in genus  Cellulose is the most abundant and widely used biopolymer on earth, most of which is produced by plants ,2. In add-glucan  with the aid of cellulose synthases [Komagataeibacter xylinus studied by Singhsa et al. [In the presence of glucokinase, phosphoglucomutase and uridine triphosphate (UTP)-glucose-1-phosphate uridylyltransferase, glucose is transformed to glucose-6-phosphate, glucose-1-phosphate, uridine diphosphate (UDP)-glucose, and finally to unbranched β-1,4-ynthases ,11. Besia et al.  showed ga et al. ,14,15. Ua et al. . More ofa et al. ,20,21,22Since the carbon sources have different molecular weights, chemical structure, and bioavailability, it can result in varied BC production rates and structural characteristics ,23. AmonKomagataeibacter sp. W1, and the BC morphology, fibril distribution, purity, and functional groups were well characterized [Our previous study reported a BC producer cterized . To undecterized . HoweverKomagataeibacter sp. W1 used in this study was isolated from a vinegar fermentation tank in Quanzhou, China [u, China .6H8O7·H2O), 0.5% yeast extract, 0.5% peptone, 0.68% Na2HPO4·12H2O, 0.115% C6H8O7·H2O and 0.051% MgSO4·7H2O [Hestrin–Schramm (HS) medium was used as the basic medium, which consisted of 2% glucose , ethanol (C2H6O), fructose (C6H12O6), glycerol (C3H8O3), lactose (C12H22O11), mannitol (C6H14O6), and sucrose (C12H22O11), respectively. The structures of various carbon sources used in this study are given in Before testing the effects of carbon sources on BC production in strain W1, the bacteria were pre-cultured in basic medium at 30 °C for 24 h under agitated condition (180 rpm) and at 30 °C for 7 days under static conditions. After that, the Erlenmeyer flask was vibrated rapidly to separate the bacteria from BC membranes. The established suspension was concentrated at 8000 The BC on the HS medium surface was collected and washed with Milli-Q water to remove medium components. To eliminate bacterial cells inside the BC membranes or on the BC surface, the samples were boiled at 100 °C for 2 h in a 0.1 M NaOH bath and anther 2 h in a Milli-Q water bath . The pre–1) [cem is dry weight of BC (g) and cam is the weight of carbon source spiked in media (g).BC production was recorded as the dry weight within the volume of medium in liter (g L–1) . The BC Scanning electron microscopy , X-ray diffraction  and Fourier transform infrared  spectroscopy were used to characterize BC.Before analysis, the dried BC was treated by spray-gold and observed by SEM with the spot of 3.0, high voltage of 15 KeV and magnification of 2000×. Subsequently, 100 nanofibrils of the BC were used for diameter calculation by using a Nano Measurer 1.2 . In this study, each 10 nm was set as a group and the data were presented as % of total nanofibrils. To have a full prediction of theoretical diameter distribution, the statistical histograms were plotted by using OriginPro 9.0 .Ka radiation (λ = 0.15406 nm) at a voltage of 40 kV and a filament emission of 30 mA, with 0.1° step, from 4° to 70° . A silicon zero background plate was used to avoid any peak resulting from the sample holder. The established sample holder position and both of the holder and silicon zero background plate were used for XRD analysis [d-spacing between the crystal planes and an apparent crystal size (ACS) approximation were determined using Bragg’s law and Scherrer’s formula by Equations (2) and (3), respectively [λ is the wavelength of the X-rays, θ is the angle between the plane and the diffracted or incident beam , and FWHM is the width of the peak at half the maximum height. FWHM was obtained by Integrated Peaks analysis based on the Peaks and Baseline module in OriginPro 9.0.X-ray diffraction was performed by using nickel filtered copper analysis . The d-s° to 70° θ, angle.maI is the maximum diffraction intensity of the lattice peak between 2θ of 22° to 23° and amI is the minimum diffraction intensity of the amorphous peak  between 2θ of 18° to 19°.Crystallinity index (C.I.) and percentage of crystallinity  of BC were calculated by Equations (4) and (5) :(4)C.I.−1 in the range of 4000–400 cm−1 was carried out. Baselines for each sample spectrum were normalized and cellulose αI content was calculated by Equation (6) [αA and βA are the integrated intensities  of the contributions from celluloses αI and βI, respectively.In addition to SEM and XRD analyses, FTIR was also used to evaluate the BC characteristics based on information of the functional groups and peak annotations. Specifically, attenuated total refection (ATR) mode with 32 scans per measurement and a resolution 0.5 cmtion (6) :(6)fαIRKomagataeibacter sp. W1 was plotted by using Microsoft PowerPoint 2016.To have a full insight into the mechanisms of carbon source transformation and BC biosynthesis, all associated open reading frames (orfs) based on gene prediction and Kyoto Encyclopedia of Genes and Genomes  pathway annotation were summarized. The information of the draft genome sequence and functional genes for this aim were given in the Sequence Read Archive (SRA) database  with the accession numbers PRJNA388252 (BioProject number), SAMN07173612 (BioSample number), and SRP108180 (SRA Study number), and the supplementary data of our previous study [p ≤ 0.05 using GraphPad Prism .All experiments were conducted in triplicate. The data are presented as the mean value of the triplicate with standard error. Significant differences were determined according to two-way analysis of variance (ANOVA) by Tukey’s multiple comparisons test at BC production in micro-organisms is an interesting trait as this process consumes lots of energy to produce biomaterials rather than resulting in cell multiplication . The posAfter 14 days of incubation at 30 °C, the BCs on the surface of media containing acetate, ethanol, fructose, glucose, glycerol, lactose, mannitol and sucrose were collected and pre-treated as previously described , the data vary from 0.071% to 5.29% day−1  plane of cellulose αI or the (110) plane of cellulose βI  of each peak of most of the BCs was the same, assuming that peak shifts at 2θ of 14.5o did not occur and different BCs had the same contents of cellulose αI [Iα) or (1Iβ) peak in the lactose group and the (010Iα) or (110Iβ) peak in ethanol, lactose and sucrose groups, all the corresponding d-spacing and ACSs were not available  with few cellulose II   from βI allomorph (around 3270 and 710 cm−1). The αI fractions of BCs produced from fructose, glucose, and sucrose in K. medellinensis were from 0.70 to 0.74 [A. xylinus 23769 of 0.36–0.43 [A. xylinus strains ATCC 10245, IFO 13693, IFO13772, IFO13773, IFO14815, and IFO15237 of 0.38–0.43 [Komagataeibacter sp. W1 of 0.51 for all groups in our study. However, our data were in accordance with the well-known fact that for αI it is as high as about 64% in BC [αI and βI was stable irrespective of the spiked carbon sources.As noted previously, BC characterization is often performed by using XRD and  methods ,55,56. Hositions . Molina-ositions  showed t.38–0.43 , and Kom4% in BC . We hypoIn micro-organisms, several metabolic pools are involved in BC biosynthesis. One direct pathway to produce BC is to pre-generate hexose phosphate by the phosphorylation of exogenous hexoses . To thisOur previous study obtained the genome information of strain W1 and annotated some associated enzymes in glucose transformation and BC synthesis . To explKomagataeibacter sp. W1 were also provided (To have a full understanding of carbon source metabolism and BC production in strain W1, we further provided an overview of the orfs that could be annotated to certain KEGG pathways . Based oprovided . As expeprovided , and allprovided . Althougprovided . Since mprovided . We alsoprovided . Howeverprovided .Komagataeibacter sp. W1, which is a typical BC-producer, was subjected to media spiked with various carbon sources including acetate, ethanol, fructose, glucose, lactose, mannitol, and sucrose and the BC productivity, BC characteristics and biochemical transformation pathways associated with carbon source transformation and BC production were investigated. This strain preferred to use fructose, glucose, glycerol, and mannitol for BC production, with the highest BC yield being 1.529 g L−1 on fructose. SEM analysis suggested that the membranes produced from all carbon sources were composed of nanofibrils with an average diameter of 35–50 nm, which is a typical characteristic of BC, consistent with the results from XRD and FTIR analyses. Based on genome annotation and KEGG analysis, all biochemical transformation pathways associated with the utilization of and BC production from fructose, glucose, glycerol, and mannitol were found. Our data provided suggestions to further investigations of strain W1 to produce BC by using the above carbon sources and gave clues on understanding how this strain produces BC at the metabolic pathway scale.In this study,


CR) is one of the most robust means to improve health and survival in model organisms. CR imposes a metabolic program that leads to increased stress resistance and delayed onset of chronic diseases, including cancer. In rodents, CR induces the upregulation of two NADH‐dehydrogenases, namely NAD(P)H:quinone oxidoreductase 1 (Nqo1) and cytochrome b5 reductase 3 (Cyb5r3), which provide electrons for energy metabolism. It has been proposed that this upregulation may be responsible for some of the beneficial effects of CR, and defects in their activity are linked to aging and several age‐associated diseases. However, it is unclear whether changes in metabolic homeostasis solely through upregulation of these NADH‐dehydrogenases have a positive impact on health and survival. We generated a mouse that overexpresses both metabolic enzymes leading to phenotypes that resemble aspects of CR including a modest increase in lifespan, greater physical performance, a decrease in chronic inflammation, and, importantly, protection against carcinogenesis, one of the main hallmarks of CR. Furthermore, these animals showed an enhancement of metabolic flexibility and a significant upregulation of the NAD+/sirtuin pathway. The results highlight the importance of these NAD+ producers for the promotion of health and extended lifespan.Calorie restriction ( Variations in energy demand, dietary requirements, and cellular energy metabolism contribute to the multiple factors that characterize the aging processes and modulate longevity  controls, with the exception of NQO1 in white adipose tissue and CYB5R3 in kidney and liver , and the results showed a divergence in the survival curves starting at 110 weeks of age  and 4.8% extension in 20% survival  in RedTg vs. Wt mice , and heat production were measured over a 91‐hr period. RedTg mice showed a trend toward higher RER during the first , and these differences increased significantly at the third (refed) cycle  term analysis in RedTg livers identified “Inflammatory Response,” “Cell Cycle,” and “Cell Proliferation” among the pathways that were robustly downregulated, whereas “Oxidoreductase Activity” was among the top upregulated pathways  was performed to evaluate the liver transcriptome profiles Figure a. Of a t3; Wt, 2115.6 ± 220.2 mm3; p = .27) , a model of metabolic syndrome‐associated liver cancer  and nicotinamide (in liver) in RedTg vs. Wt mice. Endogenous formation of nicotinamide can occur directly by cleavage of NAD+ by NAD+‐consuming enzymes such as sirtuins (SIRTs), CD38, and PARPs  markedly impedes the development of obesity‐dependent liver cancer ]. For the longevity study, male RedTg (n = 75) and wild‐type (Wt) (n = 64) mice were employed and their body weight was monitored monthly. The longevity study was performed in parallel with a previous study  and Wt (n = 17) mice were fed a high‐fat AIN‐93G Purified Rodent Diet [HF diet ‐ (#101920 ‐ Research Diets)] at the indicated time of the experiment, and their body weight was measured biweekly. The composition of SD and HF diets can be found in Tables NQO1‐transgenic mice overexpressing the rat e Figure  and CYB5Animals were inspected daily for health issues, and deaths were recorded for each animal. The criteria for euthanasia of moribund animals were based on an independent assessment by a veterinarian, according to AAALAC guidelines, and only cases where the condition of the animal was considered incompatible with continued survival were represented as deaths in the curves. Animals removed at sacrifice for experimental procedures were treated as censored deaths. Survival curves were plotted using the Kaplan–Meier method, which includes all animals at each time point.4.2A 6‐h fasted protocol was used for the determination of blood glucose and plasma insulin levels in mice as described in the Supporting Information.4.3OGTT and ITT were performed as previously described  spectrometer  was employed to acquire measurements of lean, fat, and fluid masses in live mice.4.6At the age of 14 months, mouse metabolic rate during a fed‐fast–refed cycle was assessed by indirect calorimetry in open‐circuit Oxymax chambers , as previously described , succinate dehydrogenase (complex II), ubiquinol:cytochrome c oxidoreductase (complex III), cytochrome c oxidase (complex IV), and citrate synthase (CS) were determined in mouse livers by spectrophotometric assays as described . Total RNA concentration and quality were measured by Nanodrop  and by Agilent Bioanalyzer RNA 6000 chip . cDNA reverse transcription and quantitative RT–PCR were carried out according to standard procedures as described in the Supporting Information. The primer sequences are presented in Table Total RNA was extracted from frozen liver tissue using TRIzol4.13Sixteen‐day‐old mice were injected intraperitoneally with DEN at a dose of 5 mg/kg body weight, and, at 21 days of age, they were switched to a HF diet for 16 months as previously described  equipped with a 72‐mm resonator coil (Bruker). Full methodological details for the detection and quantification of liver tumors are described in the Supporting Information.4.15t test using Excel 2010 . For survival studies, a log‐rank statistical test was used. Analyses were performed using GraphPad Prism v. 6 . In all experiments, results are represented as the mean ± SEM, with p ≤ .05 considered significant.Unless otherwise stated, statistical comparisons between genotypes were performed by two‐tailed Student's Accession codes: Microarray data have been deposited in the Gene Expression Omnibus database under accession code GSE108379.The authors declare no conflict of interest.K.J.P. A.D.R., and R.de C designed all experiments. K.J.P and A.M.M performed the longevity study. A.D.R., M.L., J.G., and F.F. carried out whole‐body physiology and physical performance studies. M.L. performed ELISAs. J.G. and H.M. performed MRI data analysis [tumor burden in liver of DEN‐treated mice]. M.A.A. performed metabolomics analysis. A.D.R, M.L, J.G., M.C.R., and A.S.P. carried out Western blot, microarray, and qPCR analyses. K. W. F. performed MRI scanning. A.D.R., A. di F., P.N., J.M.V, M.B., and R.d.C. contributed to the analysis and interpretation of the data, and the production of the manuscript. All authors critically reviewed and approved the manuscript. Click here for additional data file. Click here for additional data file.


Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthropometric indices improve diabetes prediction beyond traditional indices among elderly Chinese.Nine thousand nine hundred sixty-two elderly individuals (age ≥ 60 years old) derived from the prospective Dongfeng-Tongji cohort were included. Hazard ratio (HR) and corresponding 95% confidence interval (CI) were evaluated by Cox proportional hazard model to examine the associations between traditional anthropometric indices , novel anthropometric indices  and diabetes risk. Receiver operating characteristic (ROC) curve and area under curve (AUC) were applied to compare the novel anthropometric indices with the traditional indices in diabetes prediction.P < 0.05). Similar as men, BMI was the strongest predictor (AUC = 0.635) in women. Except for WC, the AUC of BMI was larger than WHtR, VAI, and BRI. In contrast, ABSI was not a good predictor in either men (AUC = 0.507) or women (AUC = 0.503).During mean 4.6 years of follow-up, 614 incident cases of type 2 diabetes (T2D) were identified. Significant positive associations were detected between BMI, WC, WHtR, VAI and BRI and incident T2D risk. For ABSI, no significant association was observed in either men or women. BMI was the strongest predictor in diabetes in men (AUC = 0.655) comparable with the other anthropometric indices  contains supplementary material, which is available to authorized users. In recent decades, type 2 diabetes (T2D) has increased rapidly and become a serious public health problem worldwide . In ChinObesity is well recognized to be an important risk factor for the development of T2D. In epidemiological studies, anthropometric indices have been used to evaluate obesity for their simplicity and usefulness. Body mass index (BMI) has been the most commonly used anthropometric measure for defining obesity recommended by World Health Organization (WHO)  .SubsequThe World Health Organization estimated that there were 600 million people aged 60 years or older in 2000, and that this number will increase to 1.2 billion in 2025 . It is kn = 7932), prevalent diabetes (n = 4344), coronary heart disease (n = 2607), stroke (n = 437) or cancer (n = 555) at baseline were excluded. Additionally, individuals were also ruled out if they had missing information on BMI (n = 292), WC (n = 56), triglyceride  or high-density lipoprotein cholesterol . After exclusion, a total of 5998 men and 3964 women were eligible for the present study.The participants in the present study were derived from the Dongfeng-Tongji (DFTJ) cohort study, which has been described elsewhere . In brieHeight and weight were measured without shoes and heavy clothes, using standard device and methods, recorded to the nearest 0.5 cm and 0.1 kg. WC was determined at midway level between the lower rib margin and the iliac-crest at minimal respiration. BMI was calculated as weight (kg) divided by height (m) squared. WHtR was calculated as dividing WC (cm) by height (cm). VAI was defined as the following formula :\documenABSI=WC(m)/ (BMI2/3 × height(m)1/2). BRI was defined as: BRI = 364.2–365.5× {1 – [(WC(m)/2π)/ (0.5 × height(m))]2}1/2.ABSI was defined as: Type 2 diabetes cases were defined as self-reported physician-diagnosed diabetes or taking diabetes medications  or fasting glucose concentration (FBG) ≥ 7.0 mmol/L according to the WHO criteria . In the Overnight fasting blood specimens were obtained and FBG level was measured by Abbott Aroset analyzer. TG, TC, HDL-c and high-density lipoprotein cholesterol (LDL-c) levels were measured by ARCHITECT Ci8200 automatic analyzer . Smokers were defined as those who smoke at least one cigarette per day for more than half a year, and smoking status was classified as never smoking, current smoking, and former smoking. Similarly, drinkers were defined as those who drink at least once per week for more than half a year, and drinking status was classified as never drinking, current drinking, and former drinking. Physical activity was defined as those who exercise at least 20 min per time regularly over the past 6 months. Education status was categorized into two levels: low level (middle or primary school or below) and high level (high school or beyond). Hypertension was defined if blood pressure ≥ 140/90 mmHg, or using antihypertensive medication or self-reported physician-diagnosed hypertension. Hyperlipidemia was defined if TC > 5.72 mmol/L or TG > 1.70 mmol/L or using lipid-lowing medication, or self-reported physician-diagnosed hyperlipidemia.t-test, Mann-Whiney U test or Chi-square test were used for comparison between groups. In the present analysis, central obesity was defined as WC ≥85 cm in men and WC ≥ 80 cm in women. The cut-off point of WHtR was 0.5 [2 was defined as normal, 24–27.9 kg/m2 overweight, and BMI ≥ 28 kg/m2 obesity. VAI, ABSI and BRI were stratified into sex-specific tertiles. Partial correlation was used to examine the linear relationship between various anthropometric indices after adjustment for age. Cox proportional hazard model was used to calculate hazard ratio (HR) and corresponding 95% confidence intervals (CIs) of incident diabetes for different anthropometric indices. Receiver operating characteristic (ROC) analysis was used to compare discrimination ability and determine optimal cut-off value. Sensitivity and specificity were calculated based on cut-off values, which were estimated using the maximized Youden index. The areas under the ROC curves (AUC) was compared by a non-parametric test [P < 0.05 was considered statistically significant.Categorical variables were presented in number (percentage) and continuous variables in mean (SD) or median. Student’s  was 0.5 . Accordiric test . StatistP < 0.001), TC (P = 0.019) and lower levels of HDL-c (P = 0.001). Moreover, the rate of drinking (P = 0.02) in participants who developed to T2D was higher than who did not. No significant difference was observed in ABSI (P = 0.441), smoking (P = 0.854), physical activity (P = 0.213), education level (P = 0.244), and family history of T2D (P = 0.09) between participants who did and who did not develop to T2D.During mean 4.6 years of follow-up, 365 incident cases of T2D in the 5998 men (14.09/1000 person-years) and 249 cases in the 3964 women (14.45/1000 person-years) were identified. Baseline characteristics of the 614 (6.2%) participants who did and 9348 (93.8%) who did not develop to T2D are shown in Table P < 0.001). The strongest correlation coefficient was found between WHtR and BRI (r = 0.87 and r = 0.83) and the weakest one between BMI and ABSI (r = − 0.047 and r = − 0.161). Similar findings were observed in both men and women.The correlation coefficients between various anthropometric indices are shown in  or women (P for trend = 0.313).WC, WHtR, BMI, VAI, and BRI were significantly associated with increased risk of T2D in both men and women after adjustment for potential confounders including age, smoking, drinking, physical activity and education. Further adjustment for hypertension, hyperlipidemia (except VAI), FBG (except VAI) and family history of T2D reduced the associations but still remained significant. In men, the hazard risks  were 1.43 (1.14–1.78) for WC, 1.43 (1.22–1.82) for WHtR, 2.59 (1.91–3.53) for BMI, 2.00 (1.49–2.67) for VAI, 0.79 for ABSI, and 1.60(1.20–2.13) for BRI. For women, the corresponding HRs (95% CI) were 1.46 (1.11–1.92), 1.51 (1.10–2.09), 2.01(1.41–2.87), 1.85 (1.31–2.61), 0.83, and 1.73 (1.21–2.46) for WC, WHtR, BMI, VAI, ABSI and BRI respectively.The relationships between anthropometric indices including WC, WHtR, BMI, VAI, ABSI, and BRI and incident T2D risk in men and women are shown in Table P < 0.05) including WC (AUC =0.629), WHtR (AUC =0.629), VAI (AUC = 0.609) and BRI (AUC = 0.629). Similar as men, BMI was the strongest predictor (AUC = 0.635) in women. Except for WC , the AUC of BMI was larger than other indices such as WHtR , VAI  and BRI . In contrast, ABSI was not a good predictor in either men (AUC = 0.507) or women (AUC = 0.503).Figure P < 0.05 vs. AUC = 0.50). For men, optimal WC cut-off point was 84.90 cm in terms of Youden index; for women, the optimal cut-off point was 81.10 cm. The optimal cut-off point of BMI in men was 25.78 kg/m2 and 24.86 kg/m2 in women. The cut-off points for WHtR were similar in men (0.512) and women (0.514). For VAI, it was 1.34 in men and 1.23 in women. The optimal cut-off point of BRI in men was 3.58 and 3.62 in women.Table To the best of our knowledge, this is the largest prospective study to compare the novel anthropometric indices with the traditional indices in the risk prediction of T2D in elderly population. BMI, WC, WHtR, VAI, BRI were positively associated with incident T2D risk in the elderly, independent of the potential confounders. For ABSI, no significant association was observed in either men or women. BMI appeared to be the strongest predictor of incident T2D risk in both elderly men and women. In comparison with traditional anthropometric indices, novel anthropometric indices did not improve prediction of T2D in elderly population.In the present study, BMI was the strongest predictor in both elderly men and women, while WC and WHtR showed similar prediction ability in men. These results were consistent with other studies. In Pima Indians, BMI was the best predictors of diabetes . In the A large amount of studies examined the associations of novel anthropometric indices such as VAI, ABSI and BRI with diabetes risk and their performances on diabetes risk prediction. In cross-sectional , 22 and For ABSI, no significant association was observed in either men or women. ABSI was not a good discriminator of T2D in the present study. Some studies observed that ABSI was positively correlated with mortality from cardiovascular diseases and cancer . StudiesBRI was significantly associated with increased risk of diabetes and potential for use as an alternative index in assessment of T2D, which was consistent with previous study . Till no2 and 24.86 kg/m2 for identifying diabetes risk in Chinese elderly men and women, respectively. Besides, in the present study WC threshold was about 85 cm for men and 81 cm for women, similar as the findings from a meta-analysis in China with WC cut-off points of 85 cm in men and 80 cm in women. While in Japan the optimal WC cutoff for abdominal obesity in men and women was 85 and 90 cm [Our study also proposed optimal cut-off points for these anthropometric indices. An obvious difference was observed in WC, VAI and BRI between men and women, suggesting that gender-specific reference values should be recommended in practice. The appropriate cutoff points to best identify T2D were not consistent among different age ranges and different population. Aging not only promotes increased body fat, but also changes its distribution. Furthermore, although numerous studies proposed alternative BMI criteria specific to Asian populations , no consnd 90 cm . Thus, Wnd 90 cm . As for To our knowledge, it is the first prospective study focused on elderly Chinese to comparing the novel indices with the traditional indices for diabetes risk prediction. Moreover, a notable strength was its prospective design, sex-specific analysis and relative large sample size. The study also has some limitations. Firstly, the duration of follow-up (mean 4.6 years) was relatively short and limited T2D cases were identified. Secondly, although a variety of relevant confounding factors were controlled, residual confounders could not be eliminated.In conclusion, significant associations were observed between BMI, WC, WHtR, VAI and BRI and incident risk of T2D in both elderly men and women. BMI was the strongest and best predictor of incident diabetes in elderly population. In comparison with traditional anthropometric indices, novel anthropometric indices did not improve prediction of T2D in elderly Chinese. Future studies should examine this issue in much larger samples and different population. The underlying mechanism also need to be further elucidated. Given that anthropometric indices are potentially modifiable factors, our findings may provide important public health implications for the prevention and management of T2D, especially in elderly population.Additional file 1:Table S1. Age-adjusted correlation coefficient among anthropometric indices in men and women. Table S2. Comparison of AUC among different anthropometric indices in men and women. Figure S1. Flowchart of the participants included in the present analysis. (PDF 91 kb)


Chinese organic fertilizer standard limits were exceeded by0.85% for Cr, 5.98% for As, 1.71% for Cd, and 4.27% for Pb. Monte Carlo simulations showed that repeated application of organic fertilizer likely significantly increased the concentrations of Zn, Cd, and As in soil compared with the soil background levels according to the Soil Environmental Quality Standards of China. As and Cr pose high risks to human health, especially as carcinogenic risk factors with a skin exposure pathway. Reducing the content of Cr, Cu, Zn, As, and Cd in organic fertilizer would be of great significance for minimizing the damage caused by trace metals.The application of organic fertilizer could be accompanied by potential hazards to soil and humans caused by trace metals. A wide survey of organic fertilizers was carried out in northern China. A total of 117 organic fertilizer samples were collected to analyze the concentrations of seven trace metals. Simulation models were used to estimate the trace metal accumulation risk in soil and non-carcinogenic and carcinogenic risks to the human body. The concentrations of trace metals varied widely (Cr: 2.74–151.15; Ni: 2.94–49.35; Cu: 0.76–378.32; Zn: 0.50–1748.01; As: 1.54–23.96; Cd: 2.74–151.15; and Pb: 1.60–151.09 mg·kg The consumption of fertilizers in China is widespread. According to the China Statistical Yearbook 1953–2017), the annual use of chemical fertilizers in cultivated land increased from 78,000 tons in 1952 to 59.841 million tons in 2016, thus improving food production [953–2017,Organic fertilizers are a beneficial source of plant nutrients and organic matter. Farmers often use it in plant production to increase productivity. However, organic fertilizers can also be a potential source of environmental pollution. There is increasing evidence that organic fertilizers can have high concentrations of trace metals such as Cr, Ni, Cu, Zn, As, Cd, and Pb. If organic fertilizers are applied to an agricultural area, some trace metals could be accumulated in agricultural lands, some of which could be transferred to the human body ,10. Therhttp://samr.cfda.gov.cn/WS01/CL1991/215896.html). Occupational exposure increases the risk to human health, and long-term exposure to trace metals increases the risk of non-carcinogenic and carcinogenic cells [Trace metals can also cause harm to human health, and occupational exposure increases the risk of this. According to the list of carcinogens published by the World Health Organization’s International Agency for Research on Cancer on 27 October 2017, As, Cd, Cr(VI), and Ni compounds are a class of carcinogens. Inorganic Pb is a Class 2A carcinogen, and Ni, Pb, and Cr(III) are Class 2B carcinogens  determination of concentrations and above-standard levels of seven trace metals  in organic fertilizers produced in different regions of northern China; (2) estimation of potential risks of organic fertilizer application to the accumulation of trace metals in the soil; and (3) simulation of potential human health risks following exposure to organic fertilizer containing trace metals.Samples were obtained from organic fertilizer factories in eight provinces in northern China from June to August 2017 . These f3–HClO4  and concentrations of Cr, Ni, Cu, Zn, As, Cd, and Pb were analyzed by inductively-coupled plasma mass spectrometry . Method blanks, duplicate samples, and soil standard samples  were added in the analytical process of each batch sample for quality assurance and control.All glassware and plastic containers were immersed in 5% (v/v) nitric acid solution for 24 h and then rinsed with ultrapure water and reserved for use. The dried samples were analyzed for trace metal contents in accordance with Wang et al. and Hseu ,26. In bTo provide meaningful information about the risk of trace metal accumulation in farmland soil caused by the application of organic fertilizers, Monte Carlo simulations were used to estimate the long-term effects of applications on seven trace metal concentrations in soil ,17.i refers to the seven trace metals, Cr, Ni, Cu, Zn, As, Cd, and Pb; −1; i, in mg·kg−1; and i in the soil, in mg·kg−1, which is the average of the study area , 2 × 103 m3·hm−2.The accumulation of trace metals in the soil was assessed by the input fluxes of organic fertilizer: 1·year−1 , which in g·cm−3 ; and V ii at pH > 7.5, according to the Soil Environmental Quality Agricultural Soil Risk Control Standards (GB15618-2018); and −1, respectively.Analysis of the trace metal content of organic fertilizers and estimation of the time (in years) required for trace metal concentration in soil to exceed limit values can be calculated using the following equation:i entering the human body through the mouth, in mg·kg−1·day−1; −1 [−1 [−2, 1.6 × 10−2, 1.00, 1.00, 1.00, 5.00 × 10−2, and 1.00, respectively [BW is the average weight of adults, 56.8 kg [LT is the human exposure time, 26,280 days [The concentration of trace metals entering the body through food can be calculated as follows: mg·day−1 ; EFa is y·year−1 ; EDa is 25 years ; βi is t 56.8 kg ; and LT 280 days .i entering the body through the skin, in mg·kg−1·day−1; −1 [2; −2 [i in the skin, 1.00 × 10−3, 1.6 × 10−2, 1.00 × 10−3, 6.00 × 10−4, 1.00 × 10−3, 1.00 × 10−3, and 2.08 × 10−5 for Cr, Ni, Cu, Zn, As, Cd, and Pb, respectively [The concentration of trace metals entering the body through the skin can be calculated as follows:  1 day−1 ; SA is ture, 18% ; η is th mg·cm−2 ; and γi −1·day−1; ω is the concentration of suspended particulates in the air, 0.15 mg·m−3 [3·day−1 [i in the lungs, 0.016, 0.3, 0.2, 0.01, and 0.15 for Ni, Cu, Zn, Cd, and Pb, respectively [−1 [−1 [The concentration of trace metals entering the body through breathing can be calculated as follows:5 mg·m−3 ; IRb is m3·day−1 ; and θi ors, 0.8 ; EFODa ry·year−1 ; fspid iors, 0.5 ; and EFIy·year−1 .The seven trace metals in the study, Cr, Ni, Cu, Zn, As, Cd, and Pb, all exhibit non-carcinogenic risks, among which Cr, Ni, As, and Cd also exhibit carcinogenic risk.i; i through route j, in mg·kg−1·day−1; j refers to the three exposure pathways of the human body, oral exposure (abbreviated as f), skin exposure (abbreviated as s) and respiratory exposure (abbreviated as b); i through exposure route j, in mg·kg−1·day−1  between organic fertilizers for different trace metals were assessed by ANOVA, and correlation analysis (Spearman’s r) was performed between the concentrations of trace metals in organic fertilizers. All statistical analyses above were performed with the SPSS 17.0 software . Monte Carlo simulations were carried out to predict the time scale (in years) during which the trace metal content of farmland soil increase from background level to the maximum permissible limit as a result of using organic fertilizer. The simulations were performed with Crystal Ball 11.1software . All figures were performed with the Microsoft Visio 2003 , ArcGIS10.2 , and Origin 2017  software. All data were expressed on a dry weight (DW) basis.The normality of variances was checked by the Shapiro–Wilk test. Significant differences (−1), with variability coefficients of 50.38–138.36%. Among the 55 samples taken from Northwest China, the trace metal contents also varied significantly , with variability coefficients of 53.17–170.92%. Among the 14 samples from Northeast China, the trace metal contents were Cr: 6.43–63.46; Ni: 4.33–19.09; Cu: 4.69–155.68; Zn: 16.09–493.15; As: 2.93–17.19; Cd: 0.05–1.00; and Pb: 4.43–29.34 mg·kg−1, with variability coefficients of 41.39–117.19%.The measured concentration of trace elements in the organic fertilizer was widely variable according to both the element analyzed and the region . Among tIt can be seen that the median contents of most trace metals in organic fertilizer in Northwest China were lower than those in other regions, due to the fact that its main raw material was cattle and sheep manure . Accordi−1) were lower than those in China (0.012–3692 mg·kg−1) and Southern China . This might be due to the fact that the raw material used in Northern China was cattle, poultry, and sheep manure instead of pig manure. The concentration of trace metals in organic fertilizers in China is significantly higher than that reported in Switzerland (0–1021 mg·kg−1), Australia (<0.02–1439 mg·kg−1), and the UK (<0.10–780 mg·kg−1). While China is working to reduce the concentration of trace metals in organic fertilizer, according to Ding et al., the mean value of trace metal content tended to decrease with time in commercial composts from 2002–2013 [The trace metal contents of organic fertilizer in Northern China  between Cr and Zn in North China, so the negative correlation between trace metals in organic fertilizer was negligible, while the trace metal concentrations were positively correlated.All the data in this paper showed a skewed distribution . SpearmaThere were differences in the correlations between different trace metals in different regions. Cr and Pb were highly correlated in research area. Except for Northeast China, Cu and Zn, and Cr and Ni, were closely correlated. The correlation between Cd and Pb was high, except in Northwest China. Moderate correlations were observed between Cr and Pb, Cu and Cd (North China), Ni and As, As and Pb, Ni and Cd, Cr and As, Ni and Pb (Northwest China), and Cr and Cd (Northeast China).A significant positive correlation between trace metals suggests a shared source. The relationship between Cu and Zn, Cr and Pb, Cr and Ni, and Cd and Pb was relatively close among the seven trace metals , and theThe long-term application of organic fertilizer containing trace metals can cause soil pollution. In order to estimate the time scale (in years) during which the trace metal content of farmland soil would increase from background level to the maximum permissible limit as a result of using organic fertilizer according to Soil Environmental Quality Standard of China (GB15618-2018), the data of annual application rate in the research area and the According to Equations (2) and (3), the times needed to double the trace metal concentrations in soil from their background levels in North China, Northwest China, and Northeast China were 39, 46, and 46 years, respectively . The sim−1, differs largely from that in the China Organic Fertilizer Standard (NY525-2012), which is 3 mg·kg−1. Therefore, this could be adjusted appropriately during standard revision. The content of Cr, Ni, and Pb were higher in the soil environmental quality standard than those found in the background value of trace metals in northern China, and the limit could be appropriately lowered.In the process of simulating the accumulation of trace metals in the soil after the application of organic fertilizer, it was found that for the entire northern region of China, Zn, Cd, and As showed the highest risks, and Zn and Cd required the most attention as they were found to have the greatest impact on trace metal accumulation in soil during the 16 years of research of Wang et al. . The higAccording to Equations (1) and (4)–(8), the non-carcinogenic risk of organic fertilizers was low, while the risk of Cr exposure on the skin was the highest . CombineThe carcinogenic risks for As and Cr were high for both oral and dermal absorption routes, while they were low or moderate for the inhalation of suspended particles . For allIn general, human health (non-carcinogenic and carcinogenic) in northern China is considered to be at high risk . Of the Reducing trace metal inputs in agricultural soils is an important strategy to protect farmland and ensure food safety. Estimation of the impact of the application of organic fertilizer on soil and human health is urgently needed to develop sound management practices and policies, which require information on the trace metals present in organic fertilizer. In this study, research was conducted in northern China to determine the metal contents in organic fertilizer.−1), Ni (2.94–49.35 mg·kg−1), Cu (0.76–378.32 mg·kg−1), Zn (0.50–1748.01 mg·kg−1), As (1.54–23.96 mg·kg−1), Cd (2.74–151.15 mg·kg−1), and Pb (1.60–151.09 mg·kg−1) were measured. The contents of trace metals varied significantly, with variability coefficients of 54.23–146.32%. High correlations were observed between Cu and Zn, Cr and Pb, Cr and Ni, Cr and Cd, Ni and Pb, Cr and As, and Cu and Cd, which might have similar sources or cause co-pollution in the environment. Compared with the trace metal limit standards for organic fertilizers in 24 regions of the world, these trace metals exceeded the limits, showing different values. The Chinese organic fertilizer standards were exceeded for Cr (by 0.85%), As (by 5.98%), Cd (by 1.71%), and Pb (by 4.27%). The maximum acceptable concentrations of the German standard were exceeded by 1.71% for Ni, 17.09% for Cu, and 35.04% for Zn.A total of 117 organic fertilizer samples were collected, and concentrations of Cr  and the dose–response relationship of non-carcinogenic and carcinogenic risk factors should also be considered. It is important to control and monitor the additions of Cr, Zn, As, and Cd in organic fertilizer.


This longitudinal cohort study aimed to create a novel prediction model for cardiovascular death with lifestyle factors. Subjects aged 40–74 years in the Japanese nationwide Specific Health Checkup Database in 2008 were included. Subjects were randomly assigned to the derivation and validation cohorts by a 2:1 ratio. Points for the prediction model were determined using regression coefficients that were derived from the Cox proportional hazards model in the derivation cohort. Models 1 and 2 were developed using known risk factors and known factors with lifestyle factors, respectively. The models were validated by comparing Kaplan-Meier curves between the derivation and validation cohorts, and by calibration plots in the validation cohort. Among 295,297 subjects, data for 120,823 were available. There were 310 cardiovascular deaths during a mean follow-up of 3.6 years. Model 1 included known risk factors. In model 2, weight gain, exercise habit, gait speed, and drinking alcohol were additionally included as protective factors. Kaplan-Meier curves matched better between the derivation and validation cohorts in model 2, and model 2 was better calibrated. In conclusion, our prediction model with lifestyle factors improved the predictive ability for cardiovascular death. The FRS is widely known as a standard tool for the 10-year probability of the incidence of CHD. Since this development, several important prediction models for CHD2 or cardiovascular (CV) events8 have been developed. These models have been used to identify high-risk patients, for developing preventive measures for those with a high risk, or for determining a high-risk population for clinical trials for intervention or prevention of CV events.The Framingham Heart Study identified several risk factors for coronary heart disease (CHD) and the Framingham Risk Score (FRS) was developed11. A risk chart was developed from the SCORE project9 that predicts the 10-year probability of all CV death in men and women. Variables included in this risk chart are age, systolic blood pressure (SBP), smoking habit, and serum total cholesterol (TC) levels or the TC/high-density lipoprotein cholesterol (HDL-C) ratio. NIPPON DATA8010 also investigated predictors of the 10-year probability of CV death in Japanese people. These predictors included serum glucose levels in addition to variables that were included in the SCORE project. However, these studies did not consider variables that were recently reported to be associated with CV events, such as the estimated glomerular filtration rate (eGFR)12 or proteinuria16. Furthermore, no studies have developed a model with various lifestyle factors. Sedentary lifestyle is closely associated with metabolic syndrome17, and subsequently with CV events18. Some of the lifestyles, such as gait speed, may reflect frailty. Gait speed is associated not only with all-cause mortality20, but also with CV death21.To the best of our knowledge, only a few studies have directly reported a prediction model for CV deathThis study aimed to develop a novel prediction model for CV death that is composed of previously known factors and lifestyle factors, and to compare this model with a model only including previously known risk factors.22. In 2008, the Japanese government started a new annual health check program  to support early diagnosis and intervention in metabolic syndrome. This study was part of an ongoing project called “Research on design of the comprehensive health care system for chronic kidney disease (CKD) based on individual risk assessment by Specific Health Checkup for all Japanese citizens aged 40–74 years old.” Databases that were included in this study were from Fukushima, Ibaraki, Osaka, Fukuoka, Miyazaki, and Okinawa Prefecture, which indicates regional diversity. Subjects described age, sex, smoking status, past history of stroke, and CHD in questionnaires. For physical data, trained staff measured height, body weight (BW), and blood pressure (BP) using a standard sphygmomanometer or an automated device on the right arm after resting for 5 min in the sitting position. For laboratory data, blood samples were collected after fasting overnight. Centrifuged samples were analyzed by an automatic clinical chemistry analyzer within 24 h. All blood samples were analyzed at local, rather than central, laboratories. Low-density lipoprotein cholesterol (LDL-C) levels were directly measured and not calculated by the Friedewald method23. The results of a urine dipstick test for proteinuria were interpreted by the medical staff and recorded as −, +/−, 1+, 2+, or 3+. Analyses were conducted by the methods for laboratory tests as recommended by the Japan Society of Clinical Chemistry. The database also included answers to lifestyle questionnaires. Details of the questionnaires are shown in Supplemental Table This was a longitudinal study based on a database from the Specific Health Checkup program. Details of this cohort have been published previouslyThe inclusion criterion was subjects aged 40–74 years old in the database. The exclusion criterion was subjects with missing data for variables for analyses.Ethical approval was obtained from respective institutional review board and the steering committee approved the protocol. The data were completely de-identified before being provided to the investigators.1, the Suita Study2, the SCORE project9, NIPPON DATA 8010, and the BioBank Japan project11 were included as previously known risk factors. These variables were age, sex, body mass index (BMI), history of stroke, history of CHD, current smoking, diabetes mellitus (DM), BP, eGFR, HDL-C, TC, and LDL-C. Proteinuria was also included in previously known risk factors because associations between proteinuria and CV diseases have been reported16. Answers to questionnaires on lifestyles were included in model 2. The outcome variable was CV death. Those who died because of CV diseases were identified using the national database of death certificates from 2008–2012, and the causes of death were classified according to International Classification of Diseases 10.Variables that were included in the prediction models derived from the Framingham Heart Study24 as follows: fasting plasma glucose levels ≥7.0 mmol/L (126 mg/dL), hemoglobin A1c National Glycohemoglobin Standardization Program ≥6.5%, or prescription of antidiabetic agents. The eGFR was calculated using a formula that was developed for the Japanese population: eGFR  = 194 × serum creatinine (Scr)−1.094 × age−0.287 and eGFR  = eGFR  × 0.73925.BMI was calculated as BW divided by the square of height in meters. Current smoking was defined as smoking more than 100 cigarettes in total or any for longer than 6 months and continued until the last month. Stroke was defined as self-reported hemorrhagic and ischemic stroke, and CHD as self-reported angina pectoris or myocardial infarction. DM was determined by American Diabetes Association criteriaThe causes of death were classified into the following categories according to ICD-10 codes; death due to stroke: I60.0, I60.6, I60.9, I61.0, I61.3, I61.4, I61.6, I61.9, I62.0, I62.9, I63.2, I63.4, I63.8, I63.9, I64, I67.9, I69.1, I69.3, death due to CHD: I20.9, I21.0, I21.9, I24.9, I25.1, I25.8, I25.9, death due to aortic dissection or rupture of an aneurysm: I71.0, I71.1, I71.2, I71.3, I71.8, acute cardiac death: I46.0, I46.1, I46.9, fatal arrhythmia: I47.2, I48, I49.0, I49.4, I49.8, I49.9, heart failure: I50.0, I50.9, and others: I10, I11.9, I12.0, I26.9, I27.0, I31.9, I33.0, I35.0, I38, I42.0, I42.2, I51.4, I51.5, I51.9, I74.3, I80.2, I81, I84.1.2, ≥18.5 kg/m2 and <25.0 kg/m2, and ≥25.0 kg/m2. BP was divided into SBP <130 mm Hg and diastolic blood pressure (DBP) <85 mm Hg, SBP of 130 to 139 mm Hg or DBP of 85 to 89 mm Hg, SBP of 140 to 159 mm Hg or DBP of 90 to 99 mm Hg, and SBP ≥160 or DBP ≥100 mm Hg. The eGFR was divided into ≥60 mL/min/1.73 m2 and <60 mL/min/1.73 m2. Proteinuria was divided into − or +/− and + or more. HDL-C levels were divided into ≤0.89 mmol/L (34 mg/dL), 0.90 to 1.28 mmol/L (35 to 49 mg/dL), 1.29 to 1.54 mmol/L (50 to 59 mg/dL), and ≥1.55 mmol/L (60 mg/dL). LDL-C levels were divided into ≤1.80 mmol/L (69 mg/dL), 1.81 to 2.58 mmol/L (70 to 99 mg/dL), 2.59 to 3.61 mmol/L (100 to 139 mg/dL), and ≥3.62 mmol/L (140 mg/dL). TC levels were divided into ≤4.13 mmol/L (159 mg/dL), 4.14 to 6.20 mmol/L (160 to 239 mg/dL), 6.21 to 7.23 mmol/L (240 to 279 mg/dL), and ≥7.24 mmol/L (280 mg/dL). The frequency of drinking was divided into 2 categories of no or rarely and sometimes or every day. BP was classified according to the FRS1, and when SBP and DBP fell into different categories, a higher category was selected. HDL-C, LDL-C, and TC levels were basically classified according to the FRS1, and Japanese Circulation Society guideline26. Variables were selected using stepwise backward elimination by the likelihood ratio test in the derivation cohort. Among variables that were selected by backward elimination, only those that were significantly associated with CV death were included in the prediction model. For a level of significance of backward selection, p value < 0.05 was used. Points in the models were generated by dividing each regression coefficient by the smallest absolute value of the regression coefficient in the prediction model and rounding up to the nearest integer.Subjects were randomly assigned to the derivation and validation cohorts by a 2:1 ratio. Data were shown as mean (SD) or number (%). Models 1 and 2 were developed using previously known risk factors and previously known risk factors plus lifestyle factors, respectively. The Cox proportional hazard model was used to develop prediction models for CV death. Continuous variables were divided into categories as follows. Age was divided into 40–49 years, 50–59 years, 60–69 years, and 70–74 years. BMI was divided into <18.5 kg/m27. The derivation and validation cohorts were divided into 3 categories  according to points in the prediction models and Kaplan-Meier curves were compared between the derivation and validation cohorts. The cut-off points between 3 risk groups were selected per each model where there were step-ups of the incidence rates of CV death.The created models were validated by following two methods2 rather than Hosmer-Lemeshow chi-square test, as the incidence rate of CV death was extremely low, which violated the chi-square test assumption28.In the validation cohort, the 3-year predicted probability of CV death was calculated using baseline survival function and regression coefficients in the derivation cohort. Calibration plots were created by ranking subjects into deciles of predicted probability and plotting the predicted and observed incidence in each group. Two models were compared by calibration slopes and RStatistical analyses were performed using the STATA software program version 15  and SPSS version 23.0 .http://www.mhlw.go.jp/file/06-Seisakujouhou-10600000-Daijinkanboukouseikagakuka/0000069410.pdf]. In the context of the guideline, the investigators shall not necessarily be required to obtain informed consent, but we made public information concerning this study on the web [http://www.fmu.ac.jp/univ/sangaku/data/koukai_2/2771.pdf] and ensured the opportunities for the research subjects to refuse utilizing their personal information.All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee at which the studies were conducted  and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was conducted according also to the Ethical Guidelines for Medical and Health Research Involving Human Subjects enacted by the Ministry of Health, Labour and Welfare of Japan [Among 295,297 subjects, data for 120,823 were available for analyses. There were no significant differences in demographics between included and excluded subjects (data not shown). Included subjects were divided into the derivation and validation cohorts by a 2:1 ratio  , 3 points; proteinuria, 1 point; and eGFR <60 mL/min/1.73 m2, 1 point. Variables included in model 2 were as follows: age of 60–69 years, 4 points; age 70–74 years, 5 points; male sex, 2 points; history of CHD, 2 points; current smoking, 2 points; DM, 2 points; BP (SBP ≥160 or DBP ≥100 mm Hg), 4 points; proteinuria, 2 points; eGFR <60 mL/min/1.73 m2, 1 point; weight gain ≥10 kg since 20 years old, −1 point; exercise habit, −1 point; fast gait speed, −1 point; and drinking alcohol (sometimes or every day), −1 point.The crude hazard ratios for CV death associated with each variable were shown in Supplemental Table 2 = 0.93) in model 1 and y = 0.95x + 0.14 (R2 = 0.94) in model 2, which suggested that model 2 was calibrated better. As a sensitivity analysis, we also created calibration plots restricted to those without a history of CHD or stroke. The calibration slope was y = 1.21x − 0.02 (R2 = 0.91) in model 1 and y = 0.88x + 0.12 (R2 = 0.88) in model 2, suggesting that our model also worked well for those without history of CV events.The distribution of points in each prediction model is shown in Supplemental Fig. In this study, 2 prediction models for CV death were developed and compared. Model 1 was composed of previously known risk factors for CV events. In model 2, an additional 4 lifestyle components  were included. Model 2 showed improved predictive ability for CV death compared with model 1.1, the Suita Study2, the SCORE project9, NIPPON DATA 8010, and the BioBank Japan project11, as well as proteinuria. These variables remained in model 1, except for cholesterol levels. The results did not change when TC or LDL-C was separately included in the model or different cut-off points were used. There are 2 possible explanations for why cholesterol was not associated with CV death in this study. First, aggressive cholesterol lowering has been recommended in recent guidelines in patients with a high risk for CV events. Therefore, patients with a high risk for CV events tend to have low TC and LDL-C levels. The FRS1 and Suita Score2, where LDL-C and TC were predictors of CHD, were based on a cohort that was recruited in the 1970s or 1990s. At that time, the target for lipid control was not so strict as that currently used. In our database, subgroup analysis in subjects who were not taking lipid-lowering agents showed that the incidence of death due to CHD tended to be higher in those with LDL-C levels ≥2.59 mmol/L (100 mg/dL) . This result suggests that LDL-C levels are a risk factor for CHD in patients who are not taking lipid-lowering agents. However, aggressive lipid lowering in high risk subgroups masked the association between LDL-C and CV death. The second possibility is that the association between LDL-C and CV events could be different from the association between LDL-C and CV death. The BioBank Japan database, which was registered in the 2000s, showed that lower TC levels, especially <4.66 mmol/L (180 mg/dL), were associated with CV death in subjects with chronic phase CV disease11. In our study, lower BMI, especially <18.5 kg/m2, was also associated with CV death. These results suggest that those with high LDL-C levels have a higher risk for CV events. However, those who develop CV events, despite having low LDL-C levels and BMI, and they are likely to have malnutrition, are more prone to death.Our prediction models were different from previous models in several points. Our model 1 considered variables  that were derived from the Framingham Heart Study2, was superior to the FRS in Japanese patients2. Although proteinuria was not included in previous prediction models for CV events, multiple studies have reported an association between proteinuria and CV events16. The data from the Second National Health and Nutrition Examination Survey (NHANES II) and the NHANES II Mortality Study indicated that urinary protein levels of 30 mg/dL to 299 mg/dL and ≥300 mg/dL are more associated with CV death compared with urinary protein levels <30 mg/dL (P = 0.02)13. A community-based cohort study in Canada also showed that, compared with normal protein levels, mild (urine dipstick trace or 1+) or heavy (urine dipstick ≥2+) proteinuria is associated with a higher incidence of myocardial infarction within every eGFR stratum15. According to the HOPE study, albuminuria was an independent risk factor for CV events, and the risk proportionally increased with the urine albumin to creatinine ratio, regardless of the presence of DM29. Furthermore, a meta-analysis demonstrated that the eGFR and proteinuria were multiplicatively associated with the risk of cardiovascular mortality16. In our study, eGFR and proteinuria were independent predictors of CV death and included in the prediction models.Our model 1 included the eGFR and proteinuria. The Suita study reported that the prediction model for CHD, which included an eGFR <60 mL/min/1.73 m30. However, a modest weight gain is consistently associated with the lowest all-cause mortality rate, and long-term weight loss, even of a mild or moderate degree, is associated with a higher mortality rate31. In our study, weight gain ≥10 kg since 20 years old was not associated with CV death in univariate analysis. However, when BMI was included in the covariates, it was significantly associated with a lower incidence of CV death. In model 1, low BMI was associated with a higher incidence of CV death, whereas in model 2, BMI was excluded from the model. The association between weight gain and a lower incidence of CV death is probably a reflection of the association between weight loss and a higher incidence of CV death. With regard to gait speed, data from NHANES 1999–2004 showed an association between slow gait speed, especially <0.8 m/s, and all-cause mortality20. According to a systematic review, 6 of 7 studies also showed that slow gait speed was associated with CV death21. With regard to exercise habit, the term “exercising to sweat lightly” indicated approximately 4 metabolic equivalents. Each 1 metabolic equivalent increase in exercise capacity conferred a 12% improvement in survival32. Exercise improves control of hypertension33 or DM34. Exercise might be a link between fast gait speed or exercise habit and a lower incidence of CV death. Additionally, those with unrecognized CHD might unintentionally protect themselves from experiencing angina by limiting physical activity and a slow gait speed might be a reflection of underlying unrecognized CV disease. Drinking alcohol (answering yes to “drinking sometimes or every day”) was associated with a lower incidence of CV death in our study. In our study, 64.3% of all drinkers consumed <20 g of alcohol, and 88.3% drank <40 g/day. Previous studies have consistently reported a J-shaped or U-shaped curve for the association between alcohol use and all-cause mortality or CV death. A small amount of alcohol intake (10–20 g/day for women and 20–40 g/day for men) was associated with lower all-cause mortality35. In another study, overall CV death was lower in subjects who reported consuming at least one drink daily compared with non-drinkers, despite the highest all-cause mortality among heavy drinkers in a large trial that included 490,000 subjects36. Suggested mechanisms for the benefit of moderate alcohol consumption are as follows: an increase in HDL-C levels, improvement in insulin sensitivity, favorable effects mediated by alterations in protein kinase, anti-inflammatory effects, an increase in adiponectin levels, an increase in fibrinolysis, a decrease in platelet aggregation and coagulation, and improved endothelial function37.Our model 2 was unique in that it included lifestyle factors. Weight gain ≥10 kg since 20 years old, exercise habit, fast gait speed, and drinking alcohol were included in this model. Unexpectedly, we found that weight gain ≥10 kg since 20 years old was associated with a lower incidence of CV death. Excessive weight gain is associated with the incidence of type 2 DMThis study has several strengths compared with previous studies. First, model 2 is unique in that it included 4 lifestyle factors. Addition of lifestyle factors improved the predictive ability for CV death. Questionnaires on lifestyle could be administered without any cost and could be useful in identifying those with a high risk for CV death. Second, a large number of subjects were included. The results of our study have generalizability because the subjects were from an unselected general population. Third, the outcome was CV death, which was a hard endpoint. CV death must have been accurate because it was ascertained by linking the database to the national database of death certificates.Our study also has a few limitations. Because this was an observational study, associations that were observed in the study do not imply causal relationships. An example of this limitation is that fast gait speed was associated with a lower incidence of CV death. However, whether walking fast prevents CV death or a slow gait speed is an indicator of unrecognized CV diseases is unknown. Additionally, lifestyle factors were based on the answers to questionnaires and not based on objective assessment.A novel prediction model for CV death was developed using lifestyle factors in addition to previously known risk factors for CV death. This model performs better than the model with previously known risk factors alone. Clinical implications of our prediction model require further investigation. External validation in different populations is also required to confirm the validity of our new model.Supplementary materials


The data of the stress-strain (σ-ε) were obtained. Based on the σ-ε data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and T and σ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a Finite Element Method (FEM) of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( It can be used in various extreme environments and widely used in the manufacture of various valves . Its hotσ of metals due to its simple form, small amount of calculation and significant reduction in the number of experiments [σ-ε curves of 7A52 aluminum alloy by using J-C model, which proves that the model can meet the accuracy requirements only under the reference deformation conditions. Wang et al. [T and σ-ε data, and verified the prediction accuracy. Bobbili et al. [T and σ-ε data. And used the average relative error (AARE) and correlation coefficient (R) to evaluate the accuracy, which proves that Arrhenius model has an accurate predictive ability. Abbasi-Bani et al. [−1, established its Arrhenius model and BP-ANN model. and compared the accuracy of two models. Han et al. [σ-ε data with friction factor and temperature, established Arrhenius model and BP-ANN model of it, and compared the accuracy of models. Peng et al. [In the past few decades, many scholars at home and abroad have done a lot of studies on the constitutive models of different materials. The J-C model is the first used to predict the eriments ,12. Sahueriments  establiseriments ,15 predieriments  calculateriments  predicteg et al.  predictek et al.  establisk et al.  proposedk et al.  establisk et al.  predictek et al.  observedk et al.  studied i et al.  studied i et al.  studied i et al.  collectei et al.  used thei et al.  proposedi et al.  integratn et al.  studied i et al.  studied i et al.  establisi et al. ,35 studii et al.  used J-Ci et al.  establisi et al.  predictei et al.  establisi et al.  studied n et al.  studied g et al.  calculatg et al.  establisσ-ε data. The J-C, modified J-C, Arrhenius and BP-ANN model of 21-4N were established respectively, and the accuracy of four models were verified. And then, the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% is obtained by using four models for finite element simulation. The results can provide theoretical basis to predict the rheological behavior of 21-4N.In this paper, based on the hot deformation behavior of 21-4N, the parameters of four models were calculated by using ϕ8 mm × 15 mm. T were set at 1273, 1333, 1393, 1453 K, −1, the maximum deformation was 0.916. ε and In this study, 21-4N (5Cr21Mn9Ni4N) was selected as the material, the composition of which is as follows: Cr: 20–22%, Mn: 8–10%, Ni: 3.25–4.5%, Si: ≤0.35%, C: 0.48–0.58%, N: 0.35–0.5%, P: ≤0.04%, S: ≤0.03%, Bal: Fe. The test was carried out on Gleeble-1500D high-temperature simulation test machine . The deformation temperature was measured by thermocouple, and the stress-strain data were collected through the computer control system. The size of the sample is ediately ,45.σ-ε relationship of metals and alloys under high T, high ε [In the 1980s, J-C model was proposed by Johnson and Cook to describe the  large ε ,47. WhenA—The yield stress, MPa; n—Strain hardening index; C—Strain rate sensitivity coefficient; T*—Dimensionless temperature; m—Temperature sensitivity coefficient; −1; rT—Reference temperature, K; B—Strain hardening parameter, MPa; mT—Melting temperature, K. Therefore, J-C model can be converted as:In the equation: A, B, n, C and m.From Equation (2), the J-C model contents three parts ,49: straε of 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, T of 1273, 1333, 1393, 1453 K and −1 was studied. The rT in this study was set as 1273 K, and the −1. Under this condition, the yield stress of 21-4N is 107 MPa (A = 107 MPa). The melting temperature of 21-4N was set as 1727 K (mT = 1727 K).The J-C model of 21-4N with T = rT = 1273 K and When Converting and taking the logarithm of Equation (3), and Equation (4) can be obtained:σ − A) is linear with lnε. σ − A) and lnε under different ε. The slope is n, and the intercept is lnB. By calculation, n and B were obtained: n = −0.3642, B = 37.3238 MPa.From Equation (4), ln:Taking T = rT = 1273 K, the σ is neglected. Therefore, the Equation (2) can be converted as Equation (6):When Converting the Equation (6):C and the intercept is 1. ε and C was obtained: C = 0.1449.From Equation (7), When Converting the Equation (8):ε and T. By calculation, m was obtained: m = 0.4223.From Equation (9), The J-C model of 21-4N is obtained by substituting the above calculated parameters into Equation (2):ε), strain rate (T) do not affect each other. However, it is found that J-C model has a higher accuracy only under the reference deformation condition by comparing the stresses which were obtained by the Equation (10) with the experimental stresses . When the deformation condition is more and more different from the reference deformation condition, the prediction accuracy will be lower and lower. For the purpose of improving the accuracy of J-C model, Lin, Mirza, Li, Hou et al. [Obviously, J-C model is established under the assumption that strain  are solved. σ-ε data in the ε range of 0.05–0.8 and the curve fitted by Equation (12). The values of 1A, B1, B2 and B3 in Equation (12) can be obtained by calculation  can be written as:When Converting the Equation (13):C1. C1 was obtained: C1 = 0.2002.From Equation (14), Converting the Equation (11):Taking the logarithm:Let Taking σ and T, There is a relationship between d Tegart ,57,58,59Among them:α, β, m meets the following relationship:−1; T—Absolute temperature, K; R—Gas parameter, 8.3145 J/(mol·K); Q—Deformation activation energy, kJ/mol; A, B, C—Material parameter; m, n—Stress index; α, β—Stress level parameter.In the equation: Q and T during hot deformation. Z parameters can be introduced to express this relationship [There is a certain functional relationship between tionship ,61,62,63Taking the flow stress function under full pressure in Equation (19) into Equation (18):Combining Equation (21) with Equation (22), the Arrhenius constitutive equation can be described with Z parameters. As shown in Equation (23):T, lnZ and m, β, n, n1 and lnC were obtained. The values of α and Q can be obtained by calculation. The results were shown in The three expressions in Equation (19) were respectively taken into Equation (18), then taking the logarithm of the equations. The linear relationships between m, β, α, n, Q, lnC and ε.m, β, α, n, Q, lnC and ε. The values of m, β, α, n, Q, and lnC under different strains can be calculated by the Equation (24), and the σ under different strains can be obtained correspondingly.The curves in the figure were respectively fitted by the sixth-order polynomial form shown by the Equation (24), and the fitting results were shown in The BP model is an artificial network that is calculated and trained according to error back propagation. Its Schematic diagram is shown in When 21-4N undergoes high temperature degeneration, its constitutive relationship can be expressed as:ε, T. Therefore, there are three input components and one output component in the BP-ANN model.From Equation (25), the σ is a function of T and σ [Since the components of the input layer have large differences in numerical values, for the purpose of data concentration on one or more neurons which causes the low accuracy of the model. It is necessary to normalize the input data. In general, the Equation (26) is often used to normalize T and σ ,70:(26)Ymin, Xmax—The extreme values of X; Y—X normalized vector value.In the equation: X—Raw data obtained from the experiment; XEquation (27) is often used to normalize the −3. In this paper, the MATLAB software was used for programming calculation, and the BP-ANN model was trained by using the normalized data. The training function is TRAINLM, the learning function is LEARNGD. The activation function from input layer to hidden layer is TANSIG, and the returned is PURELIN. The 3 × 10 × 10 × 1 four-layer BP-ANN structure was selected for training, and the error target was set located 10The comparison of stresses between experimental and predicted of four models are shown in rT and T, J-C model has a higher accuracy than that at high −1, the model basically loses its prediction ability. When the strain rate is 10 s−1, as the deformation temperature increases, the accuracy of the model becomes lower and lower, and the prediction ability is lost when the deformation temperature is 1333 K. The T and σ and also affect each other. With the increase of T and From T and Modified J-C model improves strain hardening effect part, considers the coupling effect between the  From ε˙ ,72.Arrhenius model introduces the Z parameter in the calculation process. The Z parameter combines the coupling effect of T and σ-ε curves, indicating that the BP-ANN model has a high accuracy under the experimental conditions. Moreover, the BP-ANN model has only two hidden layers in this study, and the precision has reached 99.769%, which indicates that when the hidden layers is more than two layers, the accuracy of it will be infinitely close to 100%. The predicted stresses obtained by the BP-ANN model are basically identical to that of the experimental [rimental ,76.The average relative error (AARE) and correlation coefficient (R) is introduced to verify the accuracy of four models. The values of AARE and R more accurately reflect the linear relationship and effective information between experimental and predicted. Equations (28) and (29) show the expressions of AARE and R:−1 as an example, the four established models were input into Deform-3D software for numerical simulation. In order to verify the practicability of the four models, the Deform-3D software was used to simulation the isothermal compression process. Taking the deformation temperature of 1393 K and strain rate of 10 sT and σ.The T. As a result, when the deformation condition changes greatly, the accuracy decreases obviously.J-C model ignores the coupling effect of the T and Modified J-C model considers the coupling effect of T and σ prediction under the reasonable deformation conditions.Arrhenius model uses the Z parameter to express the coupling effect of The established BP-ANN model has two hidden layers, which is 3 × 10 × 10 × 1 topology, and the training is completed after 47 iterations. And it has a very high accuracy under the conditions allowed by the deformation conditions.All the four models can be input into the finite element software for compression test simulation, and the simulation results are not much different from the experimental results, indicating that the four models established have certain practicability.