A Novel Modified System of Simplified Chinese Criteria for Familial Hypercholesterolemia (SCCFH)

Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?

BACKGROUND

Familial hypercholesterolemia (FH), while highly prevalent, is a significantly underdiagnosed monogenic disorder. Improved detection could reduce the large number of cardiovascular events attributable to poor case finding. We aimed to assess whether machine learning algorithms outperform clinical diagnostic criteria (signs, history, and biomarkers) and the recommended screening criteria in the United Kingdom in identifying individuals with FH-causing variants, presenting a scalable screening criteria for general populations.

METHODS AND RESULTS

Analysis included UK Biobank participants with whole exome sequencing, classifying them as having FH when (likely) pathogenic variants were detected in their LDLR, APOB, or PCSK9 genes. Data were stratified into 3 data sets for (1) feature importance analysis; (2) deriving state-of-the-art statistical and machine learning models; (3) evaluating models' predictive performance against clinical diagnostic and screening criteria: Dutch Lipid Clinic Network, Simon Broome, Make Early Diagnosis to Prevent Early Death, and Familial Case Ascertainment Tool. One thousand and three of 454 710 participants were classified as having FH. A Stacking Ensemble model yielded the best predictive performance (sensitivity, 74.93%; precision, 0.61%; accuracy, 72.80%, area under the receiver operating characteristic curve, 79.12%) and outperformed clinical diagnostic criteria and the recommended screening criteria in identifying FH variant carriers within the validation data set (figures for Familial Case Ascertainment Tool, the best baseline model, were 69.55%, 0.44%, 65.43%, and 71.12%, respectively). Our model decreased the number needed to screen compared with the Familial Case Ascertainment Tool (164 versus 227).

CONCLUSIONS

Our machine learning-derived model provides a higher pretest probability of identifying individuals with a molecular diagnosis of FH compared with current approaches. This provides a promising, cost-effective scalable tool for implementation into electronic health records to prioritize potential FH cases for genetic confirmation.

2023 China Guidelines for Lipid Management

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death among urban and rural residents in China, and elevated low-density lipoprotein cholesterol (LDL-C) is a risk factor for ASCVD. Considering the increasing burden of ASCVD, lipid management is of the utmost importance. In recent years, research on blood lipids has made breakthroughs around the world, hence a revision of China guidelines for lipid management is imperative, especially since the target lipid levels in the general population vary in respect to the risk of ASCVD. The level of LDL-C, which can be regarded as appropriate in a population without frisk factors, can be considered abnormal in people at high risk of developing ASCVD. As a result, the "Guidelines for the prevention and treatment of dyslipidemia" were adapted into the "China Guidelines for Lipid Management" (henceforth referred to as the new guidelines) by an Experts' committee after careful deliberation. The new guidelines still recommend LDL-C as the primary target for lipid control, with CVD risk stratification to determine its target value. These guidelines recommend that moderate intensity statin therapy in adjunct with a heart-healthy lifestyle, be used as an initial line of treatment, followed by cholesterol absorption inhibitors or/and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, as necessary. The new guidelines provide guidance for lipid management across various age groups, from children to the elderly. The aim of these guidelines is to comprehensively improve the management of lipids and promote the prevention and treatment of ASCVD by guiding clinical practice.

2023 Chinese guideline for lipid management

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death among urban and rural residents in China, and elevated low-density lipoprotein cholesterol (LDL-C) is a risk factor for ASCVD. Considering the increasing burden of ASCVD, lipid management is of the utmost importance. In recent years, research on blood lipids has made breakthroughs around the world, hence a revision of Chinese guideline for lipid management is imperative, especially since the target lipid levels in the general population vary in respect to the risk of ASCVD. The level of LDL-C, which can be regarded as appropriate in a population without frisk factors, can be considered abnormal in people at high risk of developing ASCVD. As a result, the "Guidelines for the prevention and treatment of dyslipidemia" were adapted into the "Chinese guideline for Lipid Management" (henceforth referred to as the new guidelines) by an Experts' committee after careful deliberation. The new guidelines still recommend LDL-C as the primary target for lipid control, with cardiovascular disease (CVD) risk stratification to determine its target value. These guidelines recommend that moderate intensity statin therapy in adjunct with a heart-healthy lifestyle, be used as an initial line of treatment, followed by cholesterol absorption inhibitors or/and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, as necessary. The new guidelines provide guidance for lipid management across various age groups, from children to the elderly. The aim of these guidelines is to comprehensively improve the management of lipids and promote the prevention and treatment of ASCVD by guiding clinical practice.

Developing a Hybrid Risk Assessment Tool for Familial Hypercholesterolemia: A Machine Learning Study of Chinese Arteriosclerotic Cardiovascular Disease Patients

Background

Familial hypercholesterolemia (FH) is an autosomal-dominant genetic disorder with a high risk of premature arteriosclerotic cardiovascular disease (ASCVD). There are many alternative risk assessment tools, for example, DLCN, although their sensitivity and specificity vary among specific populations. We aimed to assess the risk discovery performance of a hybrid model consisting of existing FH risk assessment tools and machine learning (ML) methods, based on the Chinese patients with ASCVD.

Materials and Methods

In total, 5,597 primary patients with ASCVD were assessed for FH risk using 11 tools. The three best performing tools were hybridized through a voting strategy. ML models were set according to hybrid results to create a hybrid FH risk assessment tool (HFHRAT). PDP and ICE were adopted to interpret black box features.

Results

After hybridizing the mDLCN, Taiwan criteria, and DLCN, the HFHRAT was taken as a stacking ensemble method (AUC_class[94.85 ± 0.47], AUC_prob[98.66 ± 0.27]). The interpretation of HFHRAT suggests that patients aged <75 years with LDL-c >4 mmol/L were more likely to be at risk of developing FH.

Conclusion

The HFHRAT has provided a median of the three tools, which could reduce the false-negative rate associated with existing tools and prevent the development of atherosclerosis. The hybrid tool could satisfy the need for a risk assessment tool for specific populations.

Familial Hypercholesterolaemia in the Malaysian Community: Prevalence, Under-Detection and Under-Treatment

AIM

Familial hypercholesterolaemia (FH) is the most common autosomal dominant lipid disorder, leading to severe hypercholesterolaemia. Early detection and treatment with lipid-lowering medications may reduce the risk of premature coronary artery disease in FH patients. However, there is scarcity of data on FH prevalence, detection rate, treatment and control with lipid-lowering therapy in the Malaysian community.

METHODS

Community participants (n=5130) were recruited from all states in Malaysia. Blood samples were collected for lipid profiles and glucose analyses. Personal and family medical histories were collected by means of assisted questionnaire. Physical examination for tendon xanthomata and premature corneal arcus were conducted on-site. FH were clinically screened using Dutch Lipid Clinic Network Criteria.

RESULTS

Out of 5130 recruited community participants, 55 patients were clinically categorised as potential (Definite and Probable) FH, making the prevalence FH among the community as 1:100. Based on current total population of Malaysia (32 million), the estimated number of FH patients in Malaysia is 320,000, while the detection rates are estimated as 0.5%. Lipid-lowering medications were prescribed to 54.5% and 30.5% of potential and possible FH patients, respectively, but none of them achieved the therapeutic LDL-c target.

CONCLUSION

Clinically diagnosed FH prevalence in Malaysian population is much higher than most of the populations in the world. At community level, FH patients are clinically under-detected, with majority of them not achieving target LDL-c level for high-risk patients. Therefore, public health measures are warranted for early detection and treatment, to enhance opportunities for premature CAD prevention.

A Global Review on the Utility of Genetic Testing for Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is a genetic disorder of cholesterol metabolism that affects an estimated 1/250 persons in the United States and abroad. FH is hallmarked by high low-density lipoprotein (LDL) cholesterol and an increased risk of premature atherosclerotic cardiovascular disease. This review summarizes recent global evidence showing the utility of FH genetic testing across diverse populations. Clinical and other qualitative outcomes following FH genetic testing were improved FH diagnosis, treatment initiation or continued treatment, treatment modification, improved total or LDL cholesterol levels, education on lifestyle management, and genetic counseling. This summary of evidence should be considered by those seeking overall evidence and knowledge gaps on the utility of FH genetic testing from a global perspective and for certain ethnic and age populations. These findings can be used to inform insurance policies and coverage decisions for FH genetic testing, policy recommendations to reduce the clinical and public health burden of FH, clinical practice and guidelines to improve the management of FH populations, and ongoing research involving FH genetic testing. We conclude that further investigations are needed to examine: (1) non-clinical outcomes following FH genetic testing; (2) patient-reported outcomes following FH genetic testing to convey patient experiences, values, and goals; and (3) clinical outcomes following FH genetic testing in non-Caucasian and pediatric populations in the United States and abroad.