Objective:To analyze the iodine nutrition status and its related factors among adults aged 18 years and above in Zhejiang Province in 2022.Methods:A multistage stratified sampling method was used to select 4 320 adults aged 18 years and above from 16 on-site survey sites in Zhejiang Province for the study. A questionnaire was used to investigate the general demographic information and personal dietary characteristics of the study participants. Household edible salt and urine samples were collected to detect salt iodine content and urinary iodine level by using direct titration and cerium arsenate-catalyzed spectrophotometry, respectively, to evaluate the iodine nutritional status according to the standard. The multiple-ordered logistic regression model was used to analyze the factors influencing the urinary iodine concentration.Results:The age of the 4 320 study participants was (51.19±15.33) years, with males accounting for 44.44% (1 920). About 40.16% of adults (1 735) were from coastal areas and 56.37% (2 435) from urban areas. The salt iodine content, M ( Q1, Q3), of the 4 320 household edible salt samples was 21.10 (0.00, 24.16) mg/kg, including 1 662 non-iodized salt samples, 182 unqualified iodized salt samples and 2 476 qualified iodized salt samples. The rate of iodized salt coverage was 61.53%, and the rate of qualified iodized salt consumption was 57.31%. There was a statistically significant difference in the proportion of qualified iodized salt in adult households among different regions ( P<0.001), with the proportion of non-iodized salt gradually decreasing from coastal to inland areas ( χ 2trend=618.458, P<0.001). The urinary iodine concentration M ( Q1, Q3) was 137.60 (86.85, 210.60) μg/L in 4 320 adult urine samples, with the urinary iodine levels of<100, 100-199, 200-299, and≥300 μg/L accounting for 31.64% (1 367), 40.56% (1 752), 17.66% (763), and 10.14% (438), respectively. There was a nonlinear positive correlation between household salt iodine content and urinary iodine level in adults aged 18 years and above by using the χ 2 test for trend ( χ 2regression=231.10, P<0.001 and χ 2skew=28.81, P<0.001). Urinary iodine concentrations were higher in men than in women ( P=0.029) and higher in adults in rural areas than in urban areas ( P<0.001). There were statistically significant differences in the distribution of iodine nutritional status among adults of different ages, regions, and urban and rural areas (all P<0.001). The proportion of those with urinary iodine levels<100 μg/L gradually increased with age ( χ 2trend=37.493, P<0.001), and gradually decreased from coastal areas to inland areas ( χ 2trend=71.381, P<0.001). The results of the multiple-ordered logistic regression model analysis showed that compared with adults aged 18 to 44 years and male adults, those aged 45 to 59 years and female adults had lower urinary iodine levels, with OR (95% CI) of 0.75 (0.68-0.83) and 0.85 (0.76-0.95), respectively. Compared with adults in coastal and urban adults, those in sub-coastal, inland and rural adults had higher levels of urinary iodine, with OR (95% CI) of 1.89 (1.63-2.19), 2.02 (1.72-2.37) and 1.46 (1.28-1.66), respectively. Conclusion:The overall iodine nutrition level of adults aged 18 years and above in Zhejiang Province in 2022 is generally appropriate. However, there is a potential risk of iodine deficiency among adults in coastal areas.

With the rapid development of artificial intelligence, computational pathology has been seamlessly integrated into the entire clinical workflow, which encompasses diagnosis, treatment, prognosis, and biomarker discovery. This integration has significantly enhanced clinical accuracy and efficiency while reducing the workload for clinicians. Traditionally, research in this field has depended on the collection and labeling of large datasets for specific tasks, followed by the development of task-specific computational pathology models. However, this approach is labor intensive and does not scale efficiently for open-set identification or rare diseases. Given the diversity of clinical tasks, training individual models from scratch to address the whole spectrum of clinical tasks in the pathology workflow is impractical, which highlights the urgent need to transition from task-specific models to foundation models (FMs). In recent years, pathological FMs have proliferated. These FMs can be classified into three categories, namely, pathology image FMs, pathology image-text FMs, and pathology image-gene FMs, each of which results in distinct functionalities and application scenarios. This review provides an overview of the latest research advancements in pathological FMs, with a particular emphasis on their applications in oncology. The key challenges and opportunities presented by pathological FMs in precision oncology are also explored.
Humans ; Precision Medicine/methods* ; Medical Oncology/methods* ; Artificial Intelligence ; Neoplasms/pathology* ; Computational Biology/methods*

Objective:To analyze the iodine nutrition status and its related factors among adults aged 18 years and above in Zhejiang Province in 2022.Methods:A multistage stratified sampling method was used to select 4 320 adults aged 18 years and above from 16 on-site survey sites in Zhejiang Province for the study. A questionnaire was used to investigate the general demographic information and personal dietary characteristics of the study participants. Household edible salt and urine samples were collected to detect salt iodine content and urinary iodine level by using direct titration and cerium arsenate-catalyzed spectrophotometry, respectively, to evaluate the iodine nutritional status according to the standard. The multiple-ordered logistic regression model was used to analyze the factors influencing the urinary iodine concentration.Results:The age of the 4 320 study participants was (51.19±15.33) years, with males accounting for 44.44% (1 920). About 40.16% of adults (1 735) were from coastal areas and 56.37% (2 435) from urban areas. The salt iodine content, M ( Q1, Q3), of the 4 320 household edible salt samples was 21.10 (0.00, 24.16) mg/kg, including 1 662 non-iodized salt samples, 182 unqualified iodized salt samples and 2 476 qualified iodized salt samples. The rate of iodized salt coverage was 61.53%, and the rate of qualified iodized salt consumption was 57.31%. There was a statistically significant difference in the proportion of qualified iodized salt in adult households among different regions ( P<0.001), with the proportion of non-iodized salt gradually decreasing from coastal to inland areas ( χ 2trend=618.458, P<0.001). The urinary iodine concentration M ( Q1, Q3) was 137.60 (86.85, 210.60) μg/L in 4 320 adult urine samples, with the urinary iodine levels of<100, 100-199, 200-299, and≥300 μg/L accounting for 31.64% (1 367), 40.56% (1 752), 17.66% (763), and 10.14% (438), respectively. There was a nonlinear positive correlation between household salt iodine content and urinary iodine level in adults aged 18 years and above by using the χ 2 test for trend ( χ 2regression=231.10, P<0.001 and χ 2skew=28.81, P<0.001). Urinary iodine concentrations were higher in men than in women ( P=0.029) and higher in adults in rural areas than in urban areas ( P<0.001). There were statistically significant differences in the distribution of iodine nutritional status among adults of different ages, regions, and urban and rural areas (all P<0.001). The proportion of those with urinary iodine levels<100 μg/L gradually increased with age ( χ 2trend=37.493, P<0.001), and gradually decreased from coastal areas to inland areas ( χ 2trend=71.381, P<0.001). The results of the multiple-ordered logistic regression model analysis showed that compared with adults aged 18 to 44 years and male adults, those aged 45 to 59 years and female adults had lower urinary iodine levels, with OR (95% CI) of 0.75 (0.68-0.83) and 0.85 (0.76-0.95), respectively. Compared with adults in coastal and urban adults, those in sub-coastal, inland and rural adults had higher levels of urinary iodine, with OR (95% CI) of 1.89 (1.63-2.19), 2.02 (1.72-2.37) and 1.46 (1.28-1.66), respectively. Conclusion:The overall iodine nutrition level of adults aged 18 years and above in Zhejiang Province in 2022 is generally appropriate. However, there is a potential risk of iodine deficiency among adults in coastal areas.

Objective:To investigate the iodine nutrition status of pregnant women in Zhejiang Province, explore the impact of salt industry system reform on iodine nutrition level of pregnant women, and provide scientific basis for prevention and treatment of iodine deficiency disorders.Methods:From 2016 to 2021, a multi-stage stratified sampling method was used to select 100 pregnant women from 90 counties (cities, districts) in Zhejiang Province each year as survey subjects. Salt samples were collected from pregnant women's families, and one random urine sample was taken for salt iodine and urinary iodine level testing, respectively.Results:A total of 56 581 samples of household edible salt were collected from pregnant women, with a median salt iodine level of 23.20 mg/kg. Among them, 7 961 were non iodized salt, 45 803 were qualified iodized salt, and 2 817 were unqualified iodized salt. The iodized salt coverage rate was 85.93% (48 620/56 581), and the qualified iodized salt consumption rate was 80.95% (45 803/56 581). The proportion of non iodized salt increased from 10.05% (897/8 928) in 2016 to 15.09% (1 461/9 679) in 2021 (χ 2trend = 95.16, P < 0.001). A total of 56 581 urine samples were collected from pregnant women, with a median urinary iodine level of 130.50 μg/L. Among them, the proportions of urinary iodine levels < 150, 150 - 249, 250 - 499, and ≥500 μg/L were 58.32% (32 996/56 581), 27.24% (15 410/56 581), 12.24% (6 926/56 581), and 2.21% (1 249/56 581), respectively. The median urinary iodine level of pregnant women in inland areas was significantly higher than that in coastal areas ( Z = 19.15, P < 0.001). Furthermore, urinary iodine levels exhibited a non-linear decline as age increased (χ 2regression = 12.65, P < 0.001; χ 2partial = 22.65, P < 0.001) and as pregnancy progressed (χ 2regression = 37.28, P < 0.001; χ 2partial = 18.89, P < 0.001). Conclusions:The overall iodine nutrition status of pregnant women in Zhejiang Province is in a state of iodine deficiency (< 150 μg/L), and there is a greater risk in coastal areas compared to inland areas. However, in the context of the reform of the salt industry system, it is still necessary to strengthen the quality supervision of iodized salt, provide scientific iodine supplementation education, promote specialized iodized salt for pregnant women, and strengthen interventions for prevention and control of iodine deficiency disorders.

Objective:To investigate the iodine nutrition status of pregnant women in Zhejiang Province, explore the impact of salt industry system reform on iodine nutrition level of pregnant women, and provide scientific basis for prevention and treatment of iodine deficiency disorders.Methods:From 2016 to 2021, a multi-stage stratified sampling method was used to select 100 pregnant women from 90 counties (cities, districts) in Zhejiang Province each year as survey subjects. Salt samples were collected from pregnant women's families, and one random urine sample was taken for salt iodine and urinary iodine level testing, respectively.Results:A total of 56 581 samples of household edible salt were collected from pregnant women, with a median salt iodine level of 23.20 mg/kg. Among them, 7 961 were non iodized salt, 45 803 were qualified iodized salt, and 2 817 were unqualified iodized salt. The iodized salt coverage rate was 85.93% (48 620/56 581), and the qualified iodized salt consumption rate was 80.95% (45 803/56 581). The proportion of non iodized salt increased from 10.05% (897/8 928) in 2016 to 15.09% (1 461/9 679) in 2021 (χ 2trend = 95.16, P < 0.001). A total of 56 581 urine samples were collected from pregnant women, with a median urinary iodine level of 130.50 μg/L. Among them, the proportions of urinary iodine levels < 150, 150 - 249, 250 - 499, and ≥500 μg/L were 58.32% (32 996/56 581), 27.24% (15 410/56 581), 12.24% (6 926/56 581), and 2.21% (1 249/56 581), respectively. The median urinary iodine level of pregnant women in inland areas was significantly higher than that in coastal areas ( Z = 19.15, P < 0.001). Furthermore, urinary iodine levels exhibited a non-linear decline as age increased (χ 2regression = 12.65, P < 0.001; χ 2partial = 22.65, P < 0.001) and as pregnancy progressed (χ 2regression = 37.28, P < 0.001; χ 2partial = 18.89, P < 0.001). Conclusions:The overall iodine nutrition status of pregnant women in Zhejiang Province is in a state of iodine deficiency (< 150 μg/L), and there is a greater risk in coastal areas compared to inland areas. However, in the context of the reform of the salt industry system, it is still necessary to strengthen the quality supervision of iodized salt, provide scientific iodine supplementation education, promote specialized iodized salt for pregnant women, and strengthen interventions for prevention and control of iodine deficiency disorders.

Objective:To analyze the iodine nutrition status of children and adolescents and influencing factors in Zhejiang Province, providing scientific basis for optimizing iodine deficiency disorders (IDD) prevention and control strategies.Methods:In June 2022, a multistage stratified sampling method was used to divide 16 counties (cities, districts, abbreviated as counties) in Zhejiang Province into three categories based on their geographical locations (average distance from the coastline): coastal areas (including Dinghai District, Jiaojiang District, Sanmen County, Cixi City and Lucheng District), sub-coastal areas (including Wuxing District, Haining City, Linping District, Fuyang District and Fenghua District), and inland areas(including Suichang County, Changshan County, Shengzhou City, Jindong District, Dongyang City and Yongjia County). One county was selected from each category, and one township (street) was selected from each county. Two administrative villages (neighborhood committees) were selected from each township (street). Ten households including all children and adolescents aged 6-17 in each household were selected from each administrative village (neighborhood committee). Demographic information and personal dietary characteristics were collected via questionnaires, while household salt and a random urine sample were tested for iodine level. Trend analysis was conducted using a χ 2trend test, and a multivariate logistic stepwise regression model was used to analyze the influencing factors of urinary iodine levels. Results:A total of 755 children and adolescents aged 6-17 were selected, including 387 males (51.26%) and 368 females (48.74%), with an age of (11.24 ± 3.32) years. There were 269 children and adolescents in coastal areas (35.63%) and 409 children and adolescents in urban areas (54.17%). A total of 755 household salt samples were collected, with a median salt iodine concentration of 21.80 mg/kg. These included 263 non-iodized salt samples, 38 unqualified iodized salt samples, and 454 qualified iodized salt samples. The coverage rate of iodized salt was 65.17% (492/755), and the consumption rate of qualified iodized salt was 60.13% (454/755). The distribution of salt iodine quality among children and adolescents in different geographical locations showed statistically significant differences (χ 2 = 111.95, P < 0.001), with the proportion of non-iodized salt gradually decreasing from coastal areas to inland areas (χ 2trend = 90.17, P < 0.001). A total of 755 urine samples were collected, with a median urinary iodine concentration of 186.60 μg/L. The proportions of urinary iodine < 100, 100-199, 200-299, and ≥300 μg/L were 16.95% (128/755), 37.62% (284/755), 24.37% (184/755), and 21.06% (159/755), respectively. The χ 2trend test revealed a nonlinear positive correlation between salt iodine level and urinary iodine level (χ 2regression = 21.98, P < 0.001; χ 2partial = 6.96, P < 0.001). The frequency distribution of urinary iodine in children and adolescents from different geographical locations and between urban and rural areas showed statistically significant differences (χ 2 = 29.63, 16.56, P < 0.001). Among them, the proportion of children and adolescents with urinary iodine < 100 μg/L gradually decreasing from coastal areas to inland areas (χ 2trend = 6.15, P = 0.013). The results of multivariate logistic regression analysis revealed that sub-coastal regions, inland regions, and urban-rural regions ( OR = 1.57, 1.53, 1.64, 95% CI: 1.11-2.24, 1.03-2.27, 1.17-2.32, P < 0.05) were significantly associated with urinary iodine levels in children and adolescents aged 6-17. Conclusions:In 2022, the iodine nutrition of children and adolescents in Zhejiang Province is generally suitable, but there is a risk of iodine deficiency among coastal children and adolescents. Geographic location and urban/rural areas are influencing factors on iodine nutrition status of children and adolescents in Zhejiang Province.

Objective:To analyze the iodine nutrition status of children and adolescents and influencing factors in Zhejiang Province, providing scientific basis for optimizing iodine deficiency disorders (IDD) prevention and control strategies.Methods:In June 2022, a multistage stratified sampling method was used to divide 16 counties (cities, districts, abbreviated as counties) in Zhejiang Province into three categories based on their geographical locations (average distance from the coastline): coastal areas (including Dinghai District, Jiaojiang District, Sanmen County, Cixi City and Lucheng District), sub-coastal areas (including Wuxing District, Haining City, Linping District, Fuyang District and Fenghua District), and inland areas(including Suichang County, Changshan County, Shengzhou City, Jindong District, Dongyang City and Yongjia County). One county was selected from each category, and one township (street) was selected from each county. Two administrative villages (neighborhood committees) were selected from each township (street). Ten households including all children and adolescents aged 6-17 in each household were selected from each administrative village (neighborhood committee). Demographic information and personal dietary characteristics were collected via questionnaires, while household salt and a random urine sample were tested for iodine level. Trend analysis was conducted using a χ 2trend test, and a multivariate logistic stepwise regression model was used to analyze the influencing factors of urinary iodine levels. Results:A total of 755 children and adolescents aged 6-17 were selected, including 387 males (51.26%) and 368 females (48.74%), with an age of (11.24 ± 3.32) years. There were 269 children and adolescents in coastal areas (35.63%) and 409 children and adolescents in urban areas (54.17%). A total of 755 household salt samples were collected, with a median salt iodine concentration of 21.80 mg/kg. These included 263 non-iodized salt samples, 38 unqualified iodized salt samples, and 454 qualified iodized salt samples. The coverage rate of iodized salt was 65.17% (492/755), and the consumption rate of qualified iodized salt was 60.13% (454/755). The distribution of salt iodine quality among children and adolescents in different geographical locations showed statistically significant differences (χ 2 = 111.95, P < 0.001), with the proportion of non-iodized salt gradually decreasing from coastal areas to inland areas (χ 2trend = 90.17, P < 0.001). A total of 755 urine samples were collected, with a median urinary iodine concentration of 186.60 μg/L. The proportions of urinary iodine < 100, 100-199, 200-299, and ≥300 μg/L were 16.95% (128/755), 37.62% (284/755), 24.37% (184/755), and 21.06% (159/755), respectively. The χ 2trend test revealed a nonlinear positive correlation between salt iodine level and urinary iodine level (χ 2regression = 21.98, P < 0.001; χ 2partial = 6.96, P < 0.001). The frequency distribution of urinary iodine in children and adolescents from different geographical locations and between urban and rural areas showed statistically significant differences (χ 2 = 29.63, 16.56, P < 0.001). Among them, the proportion of children and adolescents with urinary iodine < 100 μg/L gradually decreasing from coastal areas to inland areas (χ 2trend = 6.15, P = 0.013). The results of multivariate logistic regression analysis revealed that sub-coastal regions, inland regions, and urban-rural regions ( OR = 1.57, 1.53, 1.64, 95% CI: 1.11-2.24, 1.03-2.27, 1.17-2.32, P < 0.05) were significantly associated with urinary iodine levels in children and adolescents aged 6-17. Conclusions:In 2022, the iodine nutrition of children and adolescents in Zhejiang Province is generally suitable, but there is a risk of iodine deficiency among coastal children and adolescents. Geographic location and urban/rural areas are influencing factors on iodine nutrition status of children and adolescents in Zhejiang Province.

Objective:To analyze the iodine nutrition status of children aged 8 to 10 years in Zhejiang Province from 2016 to 2021.Methods:A multi-stage stratified sampling method was used to select non-residential children aged 8 to 10 years from 90 counties in Zhejiang Province. A total of 114 103 children were included in the study from 2016 to 2021. Direct titration method and arsenic-cerium catalytic spectrophotometry were used to detect salt iodine content and urinary iodine level, respectively, to evaluate the iodine nutritional status of children. Ultrasound was used to detect thyroid volume and analyze the current prevalence of goiter in school-age children.Results:The age of 114 103 children was (9.04 ± 0.81) years old, with 50.0% of (57 083) boys. The median of iodine content M ( Q1, Q3) in children's household salt was 23.00 (19.80, 25.20) mg/kg, including 17 242 non-iodized salt, 6 173 unqualified iodized salt, and 90 688 qualified iodized salt. The coverage rate of iodized salt was 84.89%, and the coverage rate of qualified iodized salt was 79.48%. The proportion of non-iodized salt increased from 11.85% in 2016 to 16.04% in 2021 ( χ 2trend=111.427, P<0.001). The median of urinary iodine concentration M ( Q1, Q3) in children was 182.50 (121.00, 261.00) μg/L, among which the proportions of iodine deficiency, iodine suitability, iodine over suitability, and iodine excess were 17.25% (19 686 cases), 39.21% (44 745 cases), 26.85% (30 638 cases), and 16.68% (19 034 cases), respectively. The median of urinary iodine concentration in children in inland areas [ M ( Q1, Q3): 190.90 (128.80, 269.00) μg/L] was significantly higher than that in children in coastal areas [ M ( Q1, Q3): 173.00 (113.00, 250.30) μg/L] ( P<0.001). From 2016 to 2021, a total of 39 134 ultrasound examinations were conducted, and 1 229 cases of thyroid enlargement were detected. The goiter rate was 3.14% (95% CI: 2.97%-3.32%). The incidence of goiter in children in coastal areas [3.45% (95% CI: 3.19%-3.72%), 641/18 604] was higher than that in children in inland areas [2.86% (95% CI: 2.64%-3.10%), 588/20 530] ( P=0.001). Conclusion:From 2016 to 2021, the iodine nutrition level of children aged 8-10 years in Zhejiang Province is generally suitable, and the rate of goiter in children meets the limit of iodine deficiency disease elimination standards.

With the rapid development of artificial intelligence technology, its applications in medical imaging have become increasingly extensive. This review aimed to analyze the current development status and future direction of intelligent imaging technology by investigating its application in various medical departments. To achieve this, we conducted a comprehensive search of various data sources up to 2024, including PubMed, Web of Science, and Google Scholar, based on the principle of comprehensive search. A total of 332 articles were screened, and after applying the inclusion and exclusion criteria, 56 articles were selected for this study. According to the findings, intelligent imaging technology exhibits robust image recognition capabilities, making it applicable across diverse medical imaging modalities within hospital departments. This technology offers an efficient solution for the analysis of various medical images by extracting and accurately identifying complex features. Consequently, it significantly aids in the detection and diagnosis of clinical diseases. Its high accuracy, sensitivity, and specificity render it an indispensable tool in clinical diagnostics and related tasks, thereby enhancing the overall quality of healthcare services. The application of intelligent imaging technology in healthcare significantly enhances the efficiency of clinical diagnostics, resulting in more accurate and timely patient assessments. This advanced technology offers a faster and more precise diagnostic approach, ultimately improving patient care and outcomes. This review analyzed the socioeconomic changes brought about by intelligent imaging technology to provide a more comprehensive evaluation. Also, we systematically analyzed the current shortcomings of intelligent imaging technology and its future development directions, to enable future research.
Humans ; Artificial Intelligence ; Diagnostic Imaging/methods* ; Hospitals

Objective:To evaluate left ventricular structural and functional abnormalities and vascular calcification in kidney transplant (KT) recipients, explore their influencing factors and examine the effects of mineral and bone disorders.Methods:From January 2017 to December 2019, retrospective analysis was performed for 292 KT recipients. Biochemical markers of bone metabolism, bone mineral density (BMD), left ventricular hypertrophy (LVH), left ventricular ejection fraction (LVEF), left ventricular diastolic function, coronary artery calcification (CAC) score and thoracic aortic calcification (TAC) score were assessed. Linear regression and binary Logistic regression analyses were employed for evaluating the influencing factors of cardiovascular parameters and the influence of abnormal mineral and bone metabolism.Results:Postoperative abnormalities in mineral and bone disorders were manifested mostly as hypercalcemia (8.9%, 26/292), hypophosphatemia (27.1%, 79/292), low 25-hydroxyvitamin D (25(OH)vitD) (67.0%, 196/292), hyperparathyroidismhigh parathyroid hormone (PTH) (50.6%, 148/292), elevated bone turnover markers and bone loss rate of 25%-30%. The prevalence of LVH, LVEF<50%, left ventricular diastolic dysfunction, high CAC score and high TAC score were 39.9%(116/292), 0%, 13.1%(38/292), 17.3%(50/292) and 39.9%(116/292) respectively. The results of multivariate analysis indicated that LVH was correlated positively with hypertension and serum calcium (Ca) (95% CI: 1.242-28.080, P=0.026; 95% CI: 1.714-277.584, P=0.018); LVEF was correlated positively with lumbar vertebrae BMD (95% CI: 0.000 1-0.005 5, P=0.041); Left ventricular diastolic dysfunction was correlated positively with age, diabetes and parathyroid hyperplasia/nodules (95% CI: 1.050-1.176, P<0.001; 95% CI: 2.118-43.813, P=0.003 and 95% CI: 1.419-9.103, P=0.007); High CAC score was correlated positively with recipient age and dialysis time (95% CI: 1.036-1.160, P=0.001; 95% CI: 1.009-1.041, P=0.002); High TAC score was correlated positively with age (95% CI: 1.095-1.215, P<0.001). Correlation analysis indicated that TAC was correlated positively with serum Ca ( r=0.233, P=0.003), bone-specific alkaline phosphatase (BALP)( r=0.325, P<0.001) and type Ⅰ collagen cross-linked N-terminal peptide (NTX)( r=0.204, P=0.011) and negatively with femoral neck BMD ( r=0.194, P=0.017). Conclusions:There is a high prevalence of left ventricular structural and functional abnormalities and vascular calcification. It is closely correlated with mineral and bone disorders.