Lipid Testing Research Articles

Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis

BackgroundIn standard Sequence Analysis, similar trajectories are clustered together to create a typology of trajectories, which is then often used to evaluate the association between sequence patterns and covariates inside regression models. The sampling uncertainty, which affects both the derivation of the typology and the associated regressions, is typically ignored in this analysis, an oversight that may lead to wrong statistical conclusions. We propose utilising sampling variation to derive new estimates that further inform on the association of interest.MethodsWe introduce a novel procedure to assess the robustness of regression results obtained from the standard analysis. Bootstrap samples are drawn from the data, and for each bootstrap, a new typology replicating the original one is constructed, followed by the estimation of the corresponding regression models. The bootstrap estimates are then combined using a multilevel modelling framework that mimics a meta-analysis. The fitted values from this multilevel model allow to account for the sampling uncertainty in the inferential analysis. We illustrate the methodology by applying it to the study of healthcare utilisation trajectories in a Swiss cohort of diabetic patients.ResultsThe procedure provides robust estimates for an association of interest, along with 95% prediction intervals, representing the range of expected values if the clustering and associated regressions were performed on a new sample from the same underlying distribution. It also identifies central and borderline trajectories within each cluster. Regarding the illustrative application, while there was evidence of an association between regular lipid testing and subsequent healthcare utilisation patterns in the original analysis, this is not supported in the robustness assessment.ConclusionsInvestigating the relationship between trajectory patterns and covariates is of interest in many situations. However, it is a challenging task with potential pitfalls. Our Robustness Assessment of Regression using Cluster Analysis Typologies (RARCAT) may assist in ensuring the robustness of such association studies. The method is applicable wherever clustering is combined with regression analysis, so its relevance goes beyond State Sequence Analysis.

Abstract 4138473: Detailed Lipoprotein Profiling in Assessing Coronary Calcium Score Progression and Atherosclerotic Risk

Background: Coronary calcium scoring (CCS) evaluates atherosclerotic burden and cardiovascular risk, correlating with coronary artery disease (CAD) severity. Advances in Nuclear Magnetic Resonance ( 1 H-NMR) spectroscopy for lipoprotein fraction analysis highlight precise atherogenic lipid parameter estimation. Enhanced lipid testing, including sub-fractionation assays, improves risk assessment and identifies new biochemical markers for CAD burden. Hypothesis: Specific lipid fractions and subfractions are significantly associated with CCS progression, serving as biochemical markers of CAD progression. Aim: To evaluate the association between lipid fractions and subfractions with CCS progression, aiming to improve CAD risk stratification. Methods: This study used data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). CCS progression was any positive change in CCS values over 5 years. High-field 1 H-NMR spectroscopy assessed lipoprotein fractions and subfractions, including plasma concentration, particle size, cholesterol, and triglyceride content. Descriptive and inferential statistics compared baseline characteristics. Multivariable binary logistic regression models, both unadjusted and propensity score-adjusted, assessed associations between plasma levels of lipoprotein fractions and CCS progression. Statistical significance was set at p <0.05. Results/Data: In this study of 2,986 participants, 1,008 (33.8%) experienced CCS progression over 5 years. Significant associations were found between plasma lipoprotein fractions and CCS progression. Large, medium, and very small TRLP (89-37 and 29-24 nm) were linked to increased CCS risk, while small TRLP (36-30 nm) had an inverse association. Small cLDLP (20.4-19 nm) were directly associated with CCS progression, while medium and large cLDLP (23-20.5 nm) were inversely associated. Small cHDLP (7.4-8.0 nm) increased CCS risk, whereas medium to large cHDLP (8.1-13.0 nm) had an inverse association. Specific HDL subspecies (H7P to H3P, 12.0-8.7 nm) were inversely associated with CCS progression, while H2P and H1P (7.8-7.4 nm) increased CCS risk. TRLZ (30-100 nm) levels were positively associated with CCS progression, whereas LDLZ (19-22.5 nm) and HDLZ (7.1-13 nm) were inversely associated. Conclusion(s): Plasma levels of specific lipoprotein fractions and subfractions are associated with CCS progression, providing insights into the complexity of lipoprotein characteristics in CAD.

Nonalcoholic Fatty Liver Disease Risk and Proprotein Convertase Subtilisin Kexin 9 in Familial Hypercholesterolemia Under Statin Treatment.

Fatty acids are involved in some hepatic disorders. The proprotein convertase subtilisin kexin 9 (PCSK9) inhibits the uptake of low-density lipoproteins (LDLs), which contain lipids, into the liver and may thus be associated with nonalcoholic fatty liver disease (NAFLD), a cardiovascular disorder (CVD) risk. Statins reduce blood LDL-cholesterol (LDL-C) levels and CVD risk and can attenuate the development of NAFLD while increasing blood PCSK9 levels. We investigated the correlation between PCSK9 and liver conditions in patients with familial hypercholesterolemia (FH), a CVD risk population with elevated blood LDL-C levels, under statin treatment. Blood tests for lipids, PCSK9, and liver function (aspartate aminotransferase [AST] and alanine aminotransferase [ALT]) were performed in patients with FH taking statins (n = 25, mean age = 57 years, 12% of males). The ALT:AST ratio was used as a marker of NAFLD risk. The mean LDL-C level was 3.38 mmol/L, and the median PCSK9 level was 312 ng/mL. The median ALT:AST ratio was 0.88. A significant negative correlation was observed between the PCSK9 and ALT:AST ratio (β = -0.67, p < 0.05). Their negative correlation might give a hypothetical insight into the effect of statin treatment on the development of NAFLD, in relation to PCSK9 behavior, in patients with FH.

Correlation of Anthropometry With Plasma Atherogenicity Indices in Subjects With Type 2 Diabetes Mellitus.

Introduction Diabetes mellitus is characterized by chronic hyperglycemia due to insulin deficiency, leading to complications in vital organs. Among these, dyslipidemia is common, presenting as low high-density lipoprotein cholesterol (HDL-c), high triglycerides (TG), Apolipoprotein-B (Apo-B), and small dense low-density lipoprotein (sdLDL) predominance, collectively known as diabetic dyslipidemia. To assess the atherogenic risk in individuals with type 2 diabetes, the atherogenic index of plasma (AIP) and atherogenic coefficient (AC) provide valuable insights beyond routine lipid tests. AIP, calculated as log (serum TG/serum HDL-c), correlates positively with the occurrence and severity of diabetic microvascular complications. The AC ((total cholesterol (TC)-HDL-c)/HDL-c) serves as an atherogenicity marker. Waist circumference (WC), reflecting central adiposity and body mass index (BMI), are directly related to both AIP and AC, making them useful non-invasive tools to monitor atherogenicity and predict cardiovascular disease (CVD) risk independently of each other in subjects with type 2 diabetes mellitus. Material and methods This was an observational cross-sectional study conducted in the Department of Medicine of a tertiary care hospital. It included 100 type 2 diabetes mellitus patients more than 18 years of age, including both males and females. Observation and results In our study, there were 42 (42%) males and 58 (58%) females. The mean WC of males and females were 105.40 and 100.98 cm, respectively. The mean for BMI, glycated hemoglobin (HbA1c), and urine albumin-to-creatinine ratio (UACR) was 28.83 kg/m2, 8.58%, and 100.62 mg/g, respectively. There was positive Pearson's correlation between AIP and WC of males and females (r = 0.324 and 0.269), AC and WC of males and females (r = 0.139 and 0.097), BMI and AIP (r = 0.350), BMI and AC (r = 0.214), HbA1c and AIP (r = 0.207), HbA1c and AC (r = 0.216), UACR and AIP (r = 0.218), and UACR and AC (r = 0.237). Conclusion This study concludes that there is a positive correlation between anthropometric measures, such as WC and BMI, and plasma atherogenicity indexes, including the AIP and AC. This finding suggests that clinicians can effectively use these non-invasive measurements (BMI and WC) to estimate the presence of dyslipidemia and atherogenicity in patients with type 2 diabetes mellitus during routine outpatient care. Early identification of these risk factors allows for timely lifestyle interventions such as dietary modifications and increased physical activity, which could potentially reduce the risk of future cardiovascular diseases.

Comparison of low-density lipoprotein cholesterol (LDL-c) estimates by in-lab and point-of-care devices in the context of emerging LDL-c equations

Abstract Low-density lipoprotein cholesterol (LDL-c) and lipid measurements are used for hyperlipidemia diagnosis, cardiovascular disease risk classification, and medication management. Millions of lipid panels are performed annually. Given the widespread need for lipid testing, there are two CLIA-waived point-of-care (POC) lipid testing devices. These devices were FDA-approved using the Friedewald equation to estimate LDL-c based on measurements of (triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), and total cholesterol (TC). By contrast, for laboratory-based LDL-c estimation, the Friedewald equation is being replaced by newer, more accurate equations such as the NIH-Sampson equation. These equations use the same lipid panel results to estimate LDL-c and only differ in the arithmetic of the estimation. This study evaluates how differences between the in-clinic POC machines and in-laboratory lipid measurements affect the estimated LDL-c results and are compounded by differences in the equations used for LDL-c estimation. This retrospective observational study collected deidentified lipid results (TG, HDL-c, TC, LDL-c) and demographic data (age, gender) for all samples that had lipid panel results from in-lab and POC within twenty-four hours of each other in a single hospital system between 2014 and 2023. Three hundred twenty samples were identified; samples with non-numerical values or results outside of the reportable range for TG, HDL-c, or TC were not included in subsequent LDL-c calculations using the Friedewald and NIH-Sampson equations (69 results). Patients under the age of 18 were excluded (2 results). Two hundred and forty-nine samples met inclusion criteria; this cohort showed non-significant differences in lipid, age, and gender distributions relative to all POC results tested in the same time frame. Consistent with prior reports, we observed discordance between POC and the in-lab lipid measurements, particularly for TG (Bland-Altman: bias:-23.04 mg/dL, standard deviation:61.3 mg/dL). These analytical differences may not change clinical management if the cardiovascular risk classification using estimated LDL-c is unchanged. However, the risk classification based on the in-lab versus in-clinic testing differed for 20% of patients. The most pronounced difference in LDL-c estimation occurred when the POC machine used Friedewald and in-lab testing used NIH-Sampson, and resulted in 5% more patients differentially classified. Regardless of testing platform, more patients are categorized as higher risk on an individual and population-based level with the NIH-Sampson equation. This study highlights the need for transparency in lipid panel reports regarding which LDL-c equation is used in both POC and in-lab testing formats, and the need for readily available tools for conversion between different LDL-c estimation equations.

NMR Insights into Lipoprotein Particles: analyzing correlations with traditional lipid measurements

Abstract Introduction Lipid metabolism plays a crucial role in various physiological processes. Lipid molecules are transported by lipoproteins, and the intricate involvement of lipoproteins in cardiovascular disease (CVD) is well-established, given their diverse size, density, and lipid composition. The emergence of Nuclear Magnetic Resonance (NMR) spectroscopy, a high-resolution analytical technique, promotes the assessment of lipoprotein particle numbers and sizes. Studies suggest that the measurement of total low-density lipoprotein particle numbers (LDL-P), small LDL-P (SLDL-P), and total high-density lipoprotein particle numbers (HDL-P) provides better evaluation of risk of CVD than enzymatic-based measurements of LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C). Our study examined the correlation between NMR-derived lipoprotein profiles and traditional cholesterol measurements in control and patient populations. Methods Fasting serum samples were collected from healthy male and female donors (n=148), ages 21-64 years. The analysis also included the results from &amp;gt;20,000 patients’ samples submitted to our laboratory for NMR lipid testing. We assessed LDL-P, SLDL-P, HDL-P, large HDL (LHDL-P), and HDL size (HDL-S) by NMR. In addition, total cholesterol (TC) and HDL-C were measured using traditional automated enzymatic assays (Roche Diagnostics); LDL-C was calculated. Statistical analyses, including Spearman correlation, were performed using GraphPad Prism software. Spearman coefficient (r) ≥0.75 indicated a strong correlation, r &amp;lt;0.5 indicated a weak correlation. Results HDL-C only moderately correlated with total HDL-P in samples collected from controls (rcontrols=0.52) and patients (rpatients=0.68), while showing better correlation with LHDL-P (rcontrols=0.90, rpatients=0.89). Traditional LDL-C strongly correlated with total LDL-P (rcontrols=0.76, rpatients=0.81), but showed only weak correlation with SLDL-P (rcontrols=0.42, rpatients=0.28), which is considered more atherogenic and predictive of CVD risk than LDL-C or total LDL-P. Interestingly, among various lipoprotein parameters, SLDL-P correlated the strongest with HDL-S in both study populations (rcontrols=-0.87, rpatients=-0.84). Samples with higher number of SLDL-P also had smaller size of HDL-P, although the number of total HDL-P did not differ between high vs low SLDL-P samples. In contrast, only moderate negative correlation was observed between SLDL-P and traditional HDL-C (rcontrols=-0.74, rpatients=-0.68), suggesting that the size of HDL particles might add value in evaluating CVD risk. Conclusion The NMR technology has advantage over traditional measurements in stratifying an individual’s lipoprotein milieu. It provides insights into the number of small LDL particles and size of HDL particles, which are not included in standard lipid testing and may allow for more individualized patient care. Incorporation of lipoprotein size and particle number in clinical guidelines may help physicians with assessment CVD risk and treatment monitoring.

Advanced lipid testing in children: a call for pediatric reference intervals

Abstract Introduction Abnormal serum lipid levels in childhood are associated with dyslipidemia and increased risk of cardiovascular disease in adulthood. High prevalence of childhood obesity in the US has increased concerns about the adverse effects of abnormal lipids in children, prompting recommendations for universal lipid screening in adolescents. While specialized analysis of lipoproteins by nuclear magnetic resonance (NMR) has been predominately used in adults with risk factors for CVD, our laboratory also receives pediatric samples for NMR testing, most commonly associated with diagnostic codes for hyperlipidemia and/or abnormal weight gain. Currently adult-based reference ranges for the NMR lipoprotein measurements are used in children. The objective of this pilot study was to compare lipoprotein profiles of healthy adults and children and to assess the impact of reference intervals for children on clinical interpretation of pediatric results. Methods Serum samples were collected from 60 healthy children, ages 1 – 17 years, and 148 healthy adults, ages 19 – 64 years old. NMR lipoprotein analysis was performed using the Axinon lipoFIT by NMR assay on a 600 MHz NMR (Bruker). Statistical analysis was conducted in Excel and GraphPad Prism. Reference intervals (RIs) were determined in EP Evaluator as the central 95% interval. Results The levels of atherogenic low-density lipoprotein particle number (LDL-P) and small LDL-P (sLDL-P) were significantly lower in children compared to adults: 783±222 vs 1212±412 nmol/L and 302±142 vs 498±306 nmol/L, respectively (p&amp;lt;0.00001). Additionally, LDL size (LDL-s) was significantly larger in children than adults (21.23±0.29 vs 21.06±0.44 nm, p=0.006), reflecting prevalence of less dense LDL-P in the former group. Pediatric samples also had lower number of anti-atherogenic high-density lipoprotein particles (HDL-P) compared to adults: 29.4±4.6 vs 33.5±4.6 µmol/L, p&amp;lt;0.00001. The significant differences in the number and size of lipoprotein particles between adults and children implies that currently used adult reference intervals might not be appropriate for evaluating pediatric NMR samples. Therefore, we estimated RIs for our heathy pediatric and adult cohorts and applied these RIs to our retrospective data of 618 samples from children 1-17 years old, which were submitted for NMR lipoprotein testing in recent years. The analysis showed that using the adult RIs, 37% and 27% of pediatric samples would not flag for abnormally elevated LDL-P and sLDL-P, respectively; but they would be flagged based on the RI derived from our pediatric cohort. Moreover, 12% pediatric samples flagged for low HDL-P would have normal HDL-P levels if pediatric RIs are used instead of adult RIs. Conclusions Use of adult reference intervals in children may result in misclassification of lipid status and underestimation or overestimation of dyslipidemia and CVD risk in children. This pilot study highlights the need for pediatric reference intervals to guide interpretation of advanced lipid testing in children.

Metabolic, cardiac, and bone health testing in patients with prostate cancer on androgen-deprivation therapy: A population-based assessment of adherence to therapeutic monitoring guidelines.

Androgen-deprivation therapy (ADT) remains a cornerstone in treatment for patients with advanced prostate cancer. ADT is associated with several adverse effects, including osteoporosis, metabolic syndrome, and cardiovascular events, leading to guidelines recommending routine testing to monitor for these toxicities. There is a lack of data assessing adherence to these recommendations. The authors conducted a retrospective cohort study using administrative data from Ontario, Canada between 2008 and 2021. They identified all older men (aged 65 years and older) who received ADT for prostate cancer using comprehensive provincial health databases. The primary outcomes were the use of testing for lipids, dysglycemia (glucose), bone health serum, and bone density between 6 weeks before and 1 year after the initiation of ADT. In total, 29,097 patients were examined, of whom 52.8% were prescribed ADT by urologists, 37.9% were prescribed ADT by radiation oncologists, 2.8% were prescribed ADT by medical oncologists, and 2.4% were prescribed ADT by other physicians. Adherence to guidelines was low: only 21.3% of patients received a bone density scan, 41.2% underwent bone health-related serum tests, 51.3% completed a lipid profile, and 65.9% underwent dysglycemia testing within 1 year of diagnosis. Overall, only 11.9% of patients received all of the recommended investigations. Adherence to testing did not appear to improve over time (2008-2021) or with guideline publication. Patient (age) and physician (specialty) factors had important associations with adherence to testing. Most patients receiving ADT for prostate cancer do not receive recommended testing to monitor for treatment-related toxicity. Further study is required to address barriers to therapeutic monitoring of men on ADT and to reduce treatment-associated adverse events.

A-213 Comparison of low-density lipoprotein cholesterol (LDL-c) estimates by in-lab and point-of-care devices in the context of emerging LDL-c equations

Abstract Background Low-density lipoprotein cholesterol (LDL-c) and lipid measurements are used for hyperlipidemia diagnosis, cardiovascular disease risk classification, and medication management. Millions of lipid panels are performed annually. Given the widespread need for lipid testing, there are two CLIA-waived point-of-care (POC) lipid testing devices. These devices were FDA-approved using the Friedewald equation to estimate LDL-c based on measurements of (triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), and total cholesterol (TC). However, in laboratory-based testing, the Friedewald equation is being replaced by newer, more accurate equations such as the NIH-Sampson equation. These equations use the same lipid panel results to estimate LDL-c and only differ in the arithmetic of the estimation. The study objective is to evaluate how differences between the in-clinic POC machines and in-laboratory lipid measurements affect the estimated LDL-c results and are compounded by differences in the equations used for LDL-c estimation. Methods This retrospective observational study collected deidentified lipid results (TG, HDL-c, TC, LDL-c) and demographic data (age, gender) for all samples that had lipid panel results from in-lab and POC within twenty-four hours of each other in a single hospital system between 2014 and 2023. Three hundred twenty samples were identified; samples with non-numerical values or results outside of the reportable range for TG, HDL-c, or TC were not included in subsequent LDL-c calculations using the Friedewald and NIH-Sampson equations (69 results). Patients under the age of 18 were excluded (2 results). Two hundred and forty-nine samples met inclusion criteria; this cohort showed non-significant differences in lipid, age, and gender distributions relative to all POC results tested in the same time frame. Results Consistent with prior reports, we observed discordance between POC and the in-lab lipid measurements, particularly for TG (Bland-Altman: bias:-23.04 mg/dL, standard deviation:61.3 mg/dL). Regardless of the testing platform, LDL-c results are higher on an individual and population-based level with the NIH-Sampson equation. These analytical differences may not change clinical management if the cardiovascular risk classification using estimated LDL-c is unchanged. However, the risk classification based on the in-lab versus in-clinic testing differed for 20-30% of patients. The most pronounced difference in LDL-c estimation occurred when the POC machine used Friedewald and in-lab testing used NIH-Sampson, and resulted in 5% more patients differentially classified. Conclusions This study highlights the need for transparency in lipid panel reports regarding which LDL-c equation is used in both POC and in-lab testing formats, and the need for readily available tools for conversion between different LDL-c estimation equations.

A-227 One Step Toward Implementation of Advanced Lipoprotien Testing in Children: A Pilot Study on Pediatric Reference Ranges

Abstract Background Abnormal serum lipid levels in childhood are associated with dyslipidemia and increased risk of cardiovascular disease in adulthood. High prevalence of childhood obesity in the US has increased concerns about the adverse effects of abnormal lipids in children, prompting recommendations for universal lipid screening in adolescents. While specialized analysis of lipoproteins by nuclear magnetic resonance (NMR) has been predominately used in adults with risk factors for CVD, our laboratory also receives pediatric samples for NMR testing, most commonly associated with diagnostic codes for hyperlipidemia and/or abnormal weight gain. Currently adult-based reference ranges for the NMR lipoprotein measurements are used in children. The objective of this pilot study was to compare lipoprotein profiles of healthy adults and children and to assess the impact of reference intervals for children on clinical interpretation of pediatric results. Methods Serum samples were collected from 60 healthy children, ages 1 - 17 years, and 148 self-identified healthy adults, ages 19 - 64 years old. NMR lipoprotein analysis was performed using the Axinon lipoFIT by NMR assay on a 600 MHz NMR (Bruker). Statistical analysis was conducted in Excel and Graph Pad Prism. Reference intervals (RIs) were determined in EP Evaluator as the central 95% interval. Results The levels of atherogenic low-density lipoprotein particle number (LDL-P) and small LDL-P (sLDL-P) were significantly lower in children compared to adults: 783±222 vs 1212±412 nmol/L and 302±142 vs 498±306 nmol/L, respectively (p&amp;lt;0.00001). Additionally, LDL size (LDL-s) was significantly larger in children than adults (21.23±0.29 vs 21.06±0.44 nm, p=0.006), reflecting prevalence of less dense LDL-P in the former group. Pediatric samples also had lower number of anti-atherogenic high-density lipoprotein particles (HDL-P) compared to adults: 29.4±4.6 vs 33.5±4.6 µmol/L, p&amp;lt;0.00001. The significant differences in the number and size of lipoprotein particles between adults and children implies that currently used adult reference intervals might not be appropriate for evaluating pediatric NMR samples. Therefore, we estimated RIs for our heathy pediatric and adult cohorts and applied these RIs to our retrospective data of 618 samples from children 1-17 years old, which were submitted for NMR lipoprotein testing in recent years. The analysis showed that using the adult RIs, 37% and 27% of pediatric samples would not flag for abnormally elevated LDL-P and sLDL-P, respectively; but they would be flagged based on the RI derived from our pediatric cohort. Moreover, 12% pediatric samples flagged for low HDL-P would have normal HDL-P levels if pediatric RIs are used instead of adult RIs. Conclusions Use of adult reference intervals in children may result in misclassification of lipid status and underestimation or overestimation of dyslipidemia and CVD risk in children. This pilot study highlights the need for pediatric reference intervals to guide interpretation of advanced lipid testing in children.

ADLM Guidance Document on the Measurement and Reporting of Lipids and Lipoproteins.

The accurate measurement of blood lipids and lipoproteins is crucial for the clinical management of atherosclerotic disease risk. Despite progress in standardization, there are still significant variations in pre-analytical requirements, methods, nomenclature, and reporting work flows. The guidance document aims to improve standardization of clinical lipid testing work flows. It provides recommendations for the components of the lipid panel, fasting requirements, reporting of results, and specific recommendations for non-high-density lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), lipoprotein(a) [Lp(a)], apolipoprotein B (apo B), point-of-care lipid testing, and LDL subfraction testing. Lipid panels should always report non-HDL-C and LDL-C calculations if possible. Fasting is not routinely required except in specific cases. Modern equations should be utilized for LDL-C calculation. These equations allow for LDL-C reporting at elevated concentrations of triglycerides and obviate the need for direct measured LDL-C in most cases.

Low prevalence of testing for apolipoprotein B and lipoprotein (a) in the real world

ObjectiveApolipoprotein B (ApoB) and lipoprotein (a) (Lp[a]) are predictors of cardiovascular disease (CVD) risk; therefore, current recommendations for CVD risk assessment and management advocate that patients receive testing for ApoB and Lp(a) in addition to the standard lipid panel. However, US guidelines around ApoB and Lp(a) testing have evolved over time and vary slightly by expert committee. The objective of this analysis was to estimate the number of insured individuals in the USA who received any component of a lipid test, or ApoB and/or Lp(a) testing, during 2019. MethodsWe conducted a cross-sectional analysis to estimate the prevalence of any component of a lipid test, ApoB, and/or Lp(a) in the USA using four different claim data sources (including Medicaid, Medicare, and commercially insured enrollees). Prevalence estimates were age-, sex-, payor-, and region-standardized to the 2019 US Annual Social and Economic Supplement of the Current Population Survey. We also described the clinical profile of patients who received lipid testing between 2019 and 2021 (cohort analysis) in Optum claims database. Enrollees were grouped into four non-mutually exclusive cohorts based on their completion of any component of the lipid panel, ApoB, Lp(a), or ApoB and Lp(a). ResultsIn the prevalence cohort, over a third (38 %) of insured adults in the USA underwent testing for any component of a lipid panel in 2019. This proportion was higher for individuals aged ≥65 years compared to younger adults (62% vs 31 %). The proportion of ApoB and Lp(a) testing represented only <1 % of testing for any component of a lipid panel. In the cohort analysis, we found that lipid testing increased with age and comorbidities. ConclusionThese data should be considered by guideline-issuing agencies and organizations to develop education campaigns encouraging more frequent use of tests beyond the standard lipid panel.

Association between dyslipidaemia knowledge & lipid testing practice among adults, a community-based study.

Various factors have been described in the literature to explain the tendency of an individual to undergo medical screenings. This study aimed to assess the association between the level of knowledge about dyslipidaemia and the frequency of lipid testing, as well as the potential impact of predisposing, enabling, and need factors on the uptake of blood lipid screening. This study was a cross-sectional survey involving 314 participants who were Malaysian residents of Taman Selatan, Klang, aged 30 and above. The study utilized a 42-item paper-based bilingual questionnaire to evaluate the predisposing, enabling, and need factors that could potentially influence the practice of lipid testing. Out of the 314 residents approached, 271 responded to the questionnaire yielding a response rate of 86.31%. The median knowledge score was 5 out of 17. Lower monthly income (OR = 3.225, 95% CI = 0.255 to 2.141), higher number of comorbidities (OR = 2.724, 95% CI = 0.037 to 2.013), higher total knowledge score (OR = 1.334, 95% CI = 0.063 to 0.512) and respondent's belief and attitude (OR = 0.813, 95% CI = -2.033 to -0.539), were found to be significantly associated with the frequency of lipid testing. In conclusion, the knowledge level regarding dyslipidaemia was below average and associated with a lower tendency to undergo frequent lipid testing. There is a need for innovative health awareness such as active educational campaigns in various settings of the community. Further qualitative studies that explore the understanding of the publics', and antecedents of their, lipid screening behaviour are required. Appropriate communication by healthcare providers should be encouraged during patient consultations for higher impact.

Trends in atherosclerotic cardiovascular disease and lipid management: a population-level observational cohort study in Wales.

European clinical guidelines recommend that patients with atherosclerotic cardiovascular disease (ASCVD), including ischaemic heart disease (IHD), stroke, and peripheral arterial disease (PAD), are prescribed lipid lowering treatment (LLT) and treated to target low-density lipoprotein cholesterol (LDL-C) levels. This study aimed to document trends in ASCVD, including treatment, monitoring, and achievement of target LDL-C. A retrospective observational population study was performed using linked healthcare data (2010-22). Over the study period, the number of patients with ASCVD increased from 181 153 to 207 747 (8882 to 9398 per 100 000). The proportion of patients prescribed LLT decreased from 75.3% in 2010 to 67.1% in 2022; high-intensity statin therapy increased from 9.4 to 25.2%, while non-high-intensity statin therapy decreased from 59.6 to 38.2%. The prescription of high-intensity statin therapy was consistently higher amongst patients with IHD (10.9% in 2010 increasing to 28.0% in 2022) than in patients with stroke (4.7-21.6%) or PAD (3.9-10.6%).The proportion of cases with documented LDL-C decreased from 58.0% in 2010 to 49.3% in 2022. Of those with documented LDL-C in 2022, 44.0% achieved LDL-C < 1.8 mmol/L, including 45.2% of those with IHD, 42.0% of those with stroke, and only 32.8% of those with PAD. Prescription of LLT, including high-intensity statin therapy, documentation of LDL-C, and achievement of target LDL-C levels was relatively low, especially in PAD patients. Although target achievement in 'tested patients' increased over time, the proportion of patients undergoing lipid testing declined. More rigorous lipid management requires prioritisation, especially for PAD and stroke patients.

Bayesian hierarchical modeling for bivariate multiscale spatial data with application to blood test monitoring

Public health spatial data are often recorded at different spatial scales (or geographic regions/divisions) and over different correlated variables. Motivated by data from the Dartmouth Atlas Project, we consider jointly analyzing average annual percentages of diabetic Medicare enrollees who have taken the hemoglobin A1c and blood lipid tests, observed at the hospital service area (HSA) and county levels, respectively. Capitalizing on bivariate relationships between these two scales is not immediate as counties are not nested within HSAs. It is well known that one can improve predictions by leveraging correlations across both variables and scales. There are very few methods available that simultaneously model multivariate and multiscale correlations. We propose three new hierarchical Bayesian models for bivariate multiscale spatial data, extending spatial random effects, multivariate conditional autoregressive (MCAR), and ordered hierarchical models through a multiscale spatial approach. We simulated data from each of the three models and compared the corresponding predictions, and found the computationally intensive multiscale MCAR model is more robust to model misspecification. In an analysis of 2015 Texas Dartmouth Atlas Project data, we produced finer resolution predictions (partitioning of HSAs and counties) than univariate analyses, determined that the novel multiscale MCAR and OH models were preferable via out-of-sample metrics, and determined the HSA with the highest within-HSA variability of hemoglobin A1c blood testing. Additionally, we compare the univariate multiscale models to the bivariate multiscale models and see clear improvements in prediction over univariate analyses.

Preventive Service Usage and New Chronic Disease Diagnoses: Using PCORnet Data to Identify Emerging Trends, United States, 2018-2022.

Data modernization efforts to strengthen surveillance capacity could help assess trends in use of preventive services and diagnoses of new chronic disease during the COVID-19 pandemic, which broadly disrupted health care access. This cross-sectional study examined electronic health record data from US adults aged 21 to 79 years in a large national research network (PCORnet), to describe use of 8 preventive health services (N = 30,783,825 patients) and new diagnoses of 9 chronic diseases (N = 31,588,222 patients) during 2018 through 2022. Joinpoint regression assessed significant trends, and health debt was calculated comparing 2020 through 2022 volume to prepandemic (2018 and 2019) levels. From 2018 to 2022, use of some preventive services increased (hemoglobin A1c and lung computed tomography, both P < .05), others remained consistent (lipid testing, wellness visits, mammograms, Papanicolaou tests or human papillomavirus tests, stool-based screening), and colonoscopies or sigmoidoscopies declined (P < .01). Annual new chronic disease diagnoses were mostly stable (6% hypertension; 4% to 5% cholesterol; 4% diabetes; 1% colonic adenoma; 0.1% colorectal cancer; among women, 0.5% breast cancer), although some declined (lung cancer, cervical intraepithelial neoplasia or carcinoma in situ, cervical cancer, all P < .05). The pandemic resulted in health debt, because use of most preventive services and new diagnoses of chronic disease were less than expected during 2020; these partially rebounded in subsequent years. Colorectal screening and colonic adenoma detection by age group aligned with screening recommendation age changes during this period. Among over 30 million patients receiving care during 2018 through 2022, use of preventive services and new diagnoses of chronic disease declined in 2020 and then rebounded, with some remaining health debt. These data highlight opportunities to augment traditional surveillance with EHR-based data.

Rates of Lipid Testing among Patients with Atherosclerotic Cardiovascular Disease within a Large Community-Based Health System

Rates of Lipid Testing among Patients with Atherosclerotic Cardiovascular Disease within a Large Community-Based Health System

The Value of Lipid Testing in Recognizing Residual Risk

The Value of Lipid Testing in Recognizing Residual Risk

Real-World Evaluation of Lipid Testing Rate and Treatment Target Attainment Among High-Risk Patients: Post Release of the 2018 AHA/ACC Practice Guidelines

Real-World Evaluation of Lipid Testing Rate and Treatment Target Attainment Among High-Risk Patients: Post Release of the 2018 AHA/ACC Practice Guidelines

A Case Report of Alcoholic Hepatitis with Megaloblastic Anemia Treated with Korean Medicine

Objectives: This study aimed to report a case of alcoholic hepatitis with megaloblastic anemia that improved with Korean medicine treatment.Methods: A 27-year-old woman with alcoholic hepatitis and megaloblastic anemia was treated with herbal medicine, acupuncture, and moxibustion. The responses before and after treatment were evaluated using blood tests, ultrasound imaging, the Checklist Individual Strength (CIS), and the numerical rating scale (NRS).Results: Post-treatment, blood tests showed improved liver function test, lipid test, and anemia work-up. Ultrasound imaging showed improvement in the superoinferior diameter of right lobe, from 13.02 to 11.09, and the fatty liver improved. The CIS score decreased from 107 to 55. Fatigue diminished from NRS 10 to NRS 3, and dizziness dropped from NRS 5 to NRS 1. The abdominal distension slightly decreased, from NRS 7 to NRS 5.Conclusion: Korean medicine treatment might be effective for treating alcoholic hepatitis with megaloblastic anemia.