Objective:To analyze the variation trends in the levels of apolipoprotein B (ApoB), the ratio of apolipoprotein B to low-density lipoprotein cholesterol (ApoB/LDL-C), and non-high density lipoprotein cholesterol (non-HDL-C) among patients and individuals undergoing physical examinations at Zhongshan Hospital, Fudan University from 2014 to 2024.Methods:Data on ApoB, LDL-C, and nonHDL-C levels were collected from individuals (the overall population) who visited or underwent physical examinations at our hospital from January 1, 2014 to December 31, 2024, as well as from individuals (the fixed population) who were tested annually over the 11-year period. The Mann-Kendall test combined with Sen slope estimation was used to analyze the trends. The abnormal rates of ApoB and non-HDL-C in both the overall and fixed populations, as well as the changes in the first and last test results over the 11 years in the fixed population, were also analyzed.Results:The overall population totaled 1 679 440 cases, aged 57 (45, 67) years, and 718 738 cases (42.8%) were female, of which 1 250 234 cases (74.4%) were in the patient population and 429 206 cases (25.6%) were in the health check population. The fixed population of 1 560 cases was 56 (45, 65) years old and 655 cases (42.0%) were female, of which 1 044 cases (66.9%) were in the patient population and 516 cases (33.1%) in the health check population. Between 2014 and 2024, the overall population ApoB decreased from 0.842 g/L to 0.822 g/L (Sen slope -0.001 g·L -1·year -1, P=0.01), the abnormality rate decreased from 24.70% to 22.03% (Sen slope -0.50%/year, P=0.06), and 69.4% of the fixed population showed no change in the subgroups of initial and final tests. ApoB/LDL-C increased from 0.824 to 0.868 (Sen slope -0.003/year, P=0.72). Non-HDL-C decreased from 3.46 mmol/L to 3.32 mmol/L (Sen slope 0.018 mmol·L -1·year -1, P=0.35), with a statistically significant upward trend since 2019 (Sen slope 0.037 mmol·L -1·year -1, P=0.008), and the anomaly rate decreased from 24.3% to 22.7% (Sen slope 0.55%/year, P=0.64), with a statistically significant upward trend since 2019 (Sen slope 1.53%/year, P=0.014), and no change in the grouping of the first and last tests in 75.0% of the fixed population. The trends in the above items were consistent between the overall population and the fixed population. 20-<40 year olds had the most significant upward trend in non-HDL-C along with the least significant downward trend in ApoB/LDL-C and ApoB. Conclusion:ApoB in the overall and fixed populations from 2014-2024 in a Shanghai hospital showed a decreasing trend, and non-HDL-C and abnormality rates showed an increasing trend from 2019 onwards.

Objective:To analyze the trend of lipid changes in patients and individuals undergoing physical examination at Zhongshan Hospital of Fudan University from 2014 to 2024, providing evidence for the formulation of cardiovascular disease prevention and control strategies.Methods:A total of 2 657 835 individuals (general population) who underwent lipid testing during medical visits or physical examinations at Zhongshan Hospital of Fudan University from January 1, 2014, to December 31, 2024, were selected. Among them, 6 234 individuals who were tested consecutively for 11 years were considered as the fixed population. Lipid levels were analyzed across different genders and age groups. Total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were analyzed. The trends in lipid changes and the abnormal rates of TC (≥5.18 mmol/L) and LDL-C (≥3.40 mmol/L) in both the general and fixed populations were statistically analyzed.Results:The median age of the general population was 53 (41, 63) years, with 1 498 533 males (56.4%); 1 149 662 individuals (43.3%) were from the physical examination group. The median age of the fixed population was 52 (39, 62) years, with 3 262 males (52.3%); 2 955 individuals (47.4%) were from the physical examination group. Over an 11-year period, the logarithmically transformed TG (lnTG) in the general population slightly increased from 1.35 mmol/L to 1.36 mmol/L (Sen slope=0.007 mmol·L -1·year -1; S=27, P=0.043). Although there were fluctuations in TC, LDL-C, and HDL-C, the trends were not statistically significant ( P>0.05). However, in the subset of the population undergoing regular health check-ups, TC showed a steady increase over time ( S=27, P=0.043). Within a fixed population over the same 11-year period, there were no statistically significant changes in lipid profiles ( P>0.05). Nevertheless, in the fixed subset undergoing regular health check-ups, both TC and lnTG exhibited an upward trend (TC: S=27, P=0.043; lnTG: S=31, P=0.020), while in the fixed subset seeking medical attention, TC and LDL-C demonstrated a downward trend (TC: S=-31, P=0.020; LDL-C: S=-27, P=0.043). Trends in lipid profiles varied among different genders and age groups. Specifically, both men and women aged 20-<40 years old showed an increase in TC, abnormal TC rates, and abnormal LDL-C rates ( P<0.05). Conversely, in the fixed population, women over 60 years old exhibited a decrease in TC, abnormal TC rates, and abnormal LDL-C rates ( P<0.05). Conclusion:During the period from 2014 to 2024, there were slight fluctuations in the average lipid levels of both the general and fixed populations. Notably, TC, abnormal TC rates, and abnormal LDL-C rates increased among men and women aged 20-<40 years old, while these parameters decreased among women over 60 years old in the fixed population.

Objective:To analyze the variation trends in the levels of apolipoprotein B (ApoB), the ratio of apolipoprotein B to low-density lipoprotein cholesterol (ApoB/LDL-C), and non-high density lipoprotein cholesterol (non-HDL-C) among patients and individuals undergoing physical examinations at Zhongshan Hospital, Fudan University from 2014 to 2024.Methods:Data on ApoB, LDL-C, and nonHDL-C levels were collected from individuals (the overall population) who visited or underwent physical examinations at our hospital from January 1, 2014 to December 31, 2024, as well as from individuals (the fixed population) who were tested annually over the 11-year period. The Mann-Kendall test combined with Sen slope estimation was used to analyze the trends. The abnormal rates of ApoB and non-HDL-C in both the overall and fixed populations, as well as the changes in the first and last test results over the 11 years in the fixed population, were also analyzed.Results:The overall population totaled 1 679 440 cases, aged 57 (45, 67) years, and 718 738 cases (42.8%) were female, of which 1 250 234 cases (74.4%) were in the patient population and 429 206 cases (25.6%) were in the health check population. The fixed population of 1 560 cases was 56 (45, 65) years old and 655 cases (42.0%) were female, of which 1 044 cases (66.9%) were in the patient population and 516 cases (33.1%) in the health check population. Between 2014 and 2024, the overall population ApoB decreased from 0.842 g/L to 0.822 g/L (Sen slope -0.001 g·L -1·year -1, P=0.01), the abnormality rate decreased from 24.70% to 22.03% (Sen slope -0.50%/year, P=0.06), and 69.4% of the fixed population showed no change in the subgroups of initial and final tests. ApoB/LDL-C increased from 0.824 to 0.868 (Sen slope -0.003/year, P=0.72). Non-HDL-C decreased from 3.46 mmol/L to 3.32 mmol/L (Sen slope 0.018 mmol·L -1·year -1, P=0.35), with a statistically significant upward trend since 2019 (Sen slope 0.037 mmol·L -1·year -1, P=0.008), and the anomaly rate decreased from 24.3% to 22.7% (Sen slope 0.55%/year, P=0.64), with a statistically significant upward trend since 2019 (Sen slope 1.53%/year, P=0.014), and no change in the grouping of the first and last tests in 75.0% of the fixed population. The trends in the above items were consistent between the overall population and the fixed population. 20-<40 year olds had the most significant upward trend in non-HDL-C along with the least significant downward trend in ApoB/LDL-C and ApoB. Conclusion:ApoB in the overall and fixed populations from 2014-2024 in a Shanghai hospital showed a decreasing trend, and non-HDL-C and abnormality rates showed an increasing trend from 2019 onwards.

Objective:To analyze the trend of lipid changes in patients and individuals undergoing physical examination at Zhongshan Hospital of Fudan University from 2014 to 2024, providing evidence for the formulation of cardiovascular disease prevention and control strategies.Methods:A total of 2 657 835 individuals (general population) who underwent lipid testing during medical visits or physical examinations at Zhongshan Hospital of Fudan University from January 1, 2014, to December 31, 2024, were selected. Among them, 6 234 individuals who were tested consecutively for 11 years were considered as the fixed population. Lipid levels were analyzed across different genders and age groups. Total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were analyzed. The trends in lipid changes and the abnormal rates of TC (≥5.18 mmol/L) and LDL-C (≥3.40 mmol/L) in both the general and fixed populations were statistically analyzed.Results:The median age of the general population was 53 (41, 63) years, with 1 498 533 males (56.4%); 1 149 662 individuals (43.3%) were from the physical examination group. The median age of the fixed population was 52 (39, 62) years, with 3 262 males (52.3%); 2 955 individuals (47.4%) were from the physical examination group. Over an 11-year period, the logarithmically transformed TG (lnTG) in the general population slightly increased from 1.35 mmol/L to 1.36 mmol/L (Sen slope=0.007 mmol·L -1·year -1; S=27, P=0.043). Although there were fluctuations in TC, LDL-C, and HDL-C, the trends were not statistically significant ( P>0.05). However, in the subset of the population undergoing regular health check-ups, TC showed a steady increase over time ( S=27, P=0.043). Within a fixed population over the same 11-year period, there were no statistically significant changes in lipid profiles ( P>0.05). Nevertheless, in the fixed subset undergoing regular health check-ups, both TC and lnTG exhibited an upward trend (TC: S=27, P=0.043; lnTG: S=31, P=0.020), while in the fixed subset seeking medical attention, TC and LDL-C demonstrated a downward trend (TC: S=-31, P=0.020; LDL-C: S=-27, P=0.043). Trends in lipid profiles varied among different genders and age groups. Specifically, both men and women aged 20-<40 years old showed an increase in TC, abnormal TC rates, and abnormal LDL-C rates ( P<0.05). Conversely, in the fixed population, women over 60 years old exhibited a decrease in TC, abnormal TC rates, and abnormal LDL-C rates ( P<0.05). Conclusion:During the period from 2014 to 2024, there were slight fluctuations in the average lipid levels of both the general and fixed populations. Notably, TC, abnormal TC rates, and abnormal LDL-C rates increased among men and women aged 20-<40 years old, while these parameters decreased among women over 60 years old in the fixed population.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Taking advantage of current advancements of the laboratory information system, patient-based real-time quality control (PBRTQC) has become a hot research topic with the potential of significantly improving the quality of clinical laboratories. PBRTQC has several advantages over conventional internal quality control, including the merit of continuous error detection, low maintenance cost, and no fear of loss of quality control due to commutability issue. This review summarized the recent research progress in PBRTQC. The review described the fundamental theories of PBRTQC and explained how to implement PBRTQC in clinical laboratories in detail. The review also provided prospective view on how to solve the current problems and what are the future research directions of PBRTQC.