Familial hypercholesterolaemia and coronary risk factors among patients with angiogram-proven premature coronary artery disease in an Asian cohort.

BACKGROUND: Familial hypercholesterolaemia (FH) patients have elevated levels of low-density lipoprotein cholesterol, rendering them at high risk of premature coronary artery disease (PCAD). However, the FH prevalence among angiogram-proven PCAD (AP-PCAD) patients and their status of coronary risk factors (CRFs) have not been reported in the Asian population.
OBJECTIVES: This study aimed to (1) determine the prevalence of clinically diagnosed FH among AP-PCAD patients, (2) compare CRFs between AP-PCAD patients with control groups, and (3) identify the independent predictors of PCAD.
METHODS: AP-PCAD patients and FH patients without PCAD were recruited from Cardiology and Specialist Lipid Clinics. Subjects were divided into AP-PCAD with FH (G1), AP-PCAD without FH (G2), FH without PCAD (G3) and normal controls (G4). Medical records were collected from the clinic database and standardised questionnaires. FH was clinically diagnosed using Dutch Lipid Clinic Network Criteria.
RESULTS: A total of 572 subjects were recruited (males:86.4%; mean±SD age: 55.6±8.5years). The prevalence of Definite, Potential and All FH among AP-PCAD patients were 6%(19/319), 16% (51/319) and 45.5% (145/319) respectively. G1 had higher central obesity, family history of PCAD and family history of hypercholesterolaemia compared to other groups. Among all subjects, diabetes [OR(95% CI): 4.7(2.9,7.7)], hypertension [OR(95% CI): 14.1(7.8,25.6)], FH [OR(95% CI): 2.9(1.5,5.5)] and Potential (Definite and Probable) FH [OR(95% CI): 4.5(2.1,9.6)] were independent predictors for PCAD. Among FH patients, family history of PCAD [OR(95% CI): 3.0(1.4,6.3)] and Definite FH [OR(95% CI): 7.1(1.9,27.4)] were independent predictors for PCAD.
CONCLUSION: Potential FH is common among AP-PCAD patients and contributes greatly to the AP-PCAD. FH-PCAD subjects have greater proportions of various risk factors compared to other groups. Presence of FH, diabetes, hypertension, obesity and family history of PCAD are independent predictors of PCAD. FH with PCAD is in very-high-risk category, hence, early management of modifiable CRFs in these patients are warranted.

Introduction

Familial hypercholesterolaemia (FH) is a hereditary disorder of lipoprotein metabolism, predominantly caused by genetic mutation of low-density lipoprotein receptor gene (LDLR). Familial hypercholesterolaemia causes elevated low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) serum levels resulting in increased risk of coronary artery disease (CAD) [1]. Globally, the prevalence of FH varies between 1:200–1:500 [2] with >50% FH patients are estimated to be residing in Asia Pacific region [3]. In Malaysia, the prevalence of heterozygous FH was reported to be ~1:100 [4,5]. With the Malaysian population size of 33 million (Department of Statistics Malaysia, 2021), it is estimated that 330,000 individuals are affected by FH, majority of whom are underdiagnosed and/or inadequately treated [5].

Dutch Lipid Clinic Network Criteria (DLCC) (World Health Organization, 1999) [6] is the most widely used FH diagnostic criteria, other than Simon Broome’s [7], US MEDPED [8] the Japanese FH Management Criteria [9] the AHA Guidelines [10] and the most recent Canada’s Make Early Diagnosis Prevent Early Death criteria [11,12]. The DLCC categorised FH patients into three categories (Definite, Probable or Possible FH), according to various criteria.

Cardiovascular disease (CVD), including CAD is the leading cause of mortality and morbidity in Malaysia [13] and globally [14], in both men and women [15]. The latest Malaysian National Health and Morbidity Survey (NHMS) 2019 reported that CAD is the leading cause of death in Malaysia, with hypertension, diabetes and hypercholesterolaemia as the major risk factors (Institute for Public Health, 2020). Coronary angiography is considered to be the gold standard for diagnosing CAD [16]. Upon confirmation with coronary angiography, CAD patients are treated with minimally invasive percutaneous coronary intervention (PCI) stenting procedure or undergo coronary artery bypass graft (CABG) if the coronary artery blockage is extensive.

While it is generally known that the main cause of CAD is atherosclerosis [17], there is no universally accepted for the onset age of PCAD. A study considered the age limit ranging from 35 to 55 years [18] while other studies considered the onset age of PCAD at ≤45 years [19] and <45 years in males and <55 years in females [20]. PCAD used in this study is defined as the occurrence of CAD at the age <55 years in men and <60 years in women [21] as it is part of the diagnostic parameter in DLCC. PCAD can be prevented by controlling modifiable risks factors such as blood pressure, cholesterol levels, smoking, diabetes, obesity, lack of physical activity, diet and stress. However, there are also non-modifiable risk factors such as age, gender, ethnicity, genetic factors and family history of PCAD and hypercholesterolaemia (HC) [22]. While preventive therapy can be customised to alter particular risk factors, individuals with established CVD are at very high risk of recurrent events. The risk of developing CAD rises with age, but in younger patients, the prevalence of these risk factors tends to differ. Smoking is by far the most frequently associated risk factor in premature CAD [23].

FH is an important cause of PCAD. Given that PCAD prevalence is increasing in Asia, and considering Malaysia has the lowest mean age of onset for PCAD compared to the developed countries [13], early detection, optimal treatment and PCAD prevention are critically important. FH in the absence of atherosclerotic cardiovascular disease (ASCVD) is grouped into high-risk category, whilst ASCVD is categorised as very high-risk category. Therefore, FH patients with PCAD are in extremely high risk and requires immediate attention and aggressive treatment. The increased lifetime exposure to elevated LDL-C amongst FH patients is closely associated with increased PCAD risk, hence, appropriate intensive lipid-lowering therapy and lifestyle modification is recommended.

Being underdiagnosed and undertreated [21], many FH patients are unaware of their condition until being admitted for acute coronary syndrome (ACS) or CAD. In Asia, FH is not routinely screened, even among angiogram-proven PCAD (AP-PCAD) patients hence, the FH data among these patients are scarce. Besides, the prevalence of FH among AP-PCAD patients, status of the other coronary risk factors amongst them in the Asian population have not been well established. Furthermore, the independent predictors of PCAD in FH patients is still unclear. Thus, the objective of this study is to (1) determine the prevalence of clinically diagnosed FH among AP-PCAD patients, (2) compare the coronary risk factors (CRFs) between AP-PCAD patients with FH (G1) and without FH (G2), FH patients without PCAD (G3) and normal controls without FH and PCAD (G4); (3) and to identify the independent predictors of PCAD among all subjects and those with FH. The identification of FH particularly among PCAD patients is vital in elevating patients’ awareness, prompt timely intervention and ensures specific preventive measures by recognising the risk factors and manage them accordingly.

Methodology

Study design and patient recruitment

This was a comparative cross-sectional study where subjects were recruited from the National Heart Institute (IJN) and Specialist Clinics (Cardiology and Lipid Clinics) and community health screening programmes from the year 2018 to 2019.

The inclusion criteria were male and female Malaysians aged ≥18 years, with AP-PCAD and voluntarily consented to participate in this study. PCAD patients (Age of onset <55 years in males and <60 years in females) were enrolled into this study. Patients were diagnosed as having AP-PCAD based on significant angiogram results, or previous history of CABG and/or PCI procedures. FH was clinically diagnosed using the Dutch Lipid Clinic Network criteria (DLCC). The exclusion criteria were non-Malaysians, pregnancy, and those with secondary hypercholesterolaemia (such as hypothyroidism, nephrotic syndrome, cholelithiasis and chronic renal disease).

Normal control subjects were collected through community health screening programmes. All Malaysians aged ≥18 years were eligible for inclusion into this study, while those with secondary hypercholesterolaemia, pregnant women and non-Malaysians were excluded.

A total of 572 individuals were recruited for the study. Subjects were divided into four groups: G1 (Group 1—PCAD with FH), G2 (Group 2—PCAD without FH), G3 (Group 3—Non-PCAD, and non-CAD, but with FH) and G4 (Group 4—normal controls, without PCAD and CAD, nor FH).

Definition of terms

Coronary artery disease was defined as those with previous medical history of an abnormal coronary angiogram with stenosis of ≥50% [24] in at least one major epicardial coronary artery, or had underwent PCI, and/or CABG [25].

Hypercholesterolaemia and hypertriglyceridaemia were defined as TC >5.2 mmol/L, and TG >1.7 mmol/L respectively. Low HDL-C was defined as <1.0 mmol/L (males) and <1.2 mmol/L (females). Severely elevated LDL-C level was defined as >4.9 mmol/L, based on high CVD risk categories, according to the 5th edition Malaysia CPG on Management of Dyslipidaemia 2017 [26]. Both type 1 and type 2 diabetes mellitus (DM) were defined as those with fasting or random plasma glucose >7.0 and >11.1 mmol/L respectively for newly diagnosed DM, or those with known or previously diagnosed DM with/ without anti-diabetic medications. Hypertension was defined as systolic blood pressure of ≥140 mmHg and/or diastolic blood pressure of ≥90 mmHg or those with known or previously diagnosed as hypertension with/ without anti-hypertensive medications.

BMI categorisation was classified into underweight, normal, overweight and obese (BMI:<18.5, 18.5–22.9, 23.0–24.9 and ≥25, respectively) [27]. Central obesity was defined as waist circumference (WC) measurement of the ≥90cm in males and ≥80cm in females [28].

Subjects were categorised into Definite, Probable, Possible or Unlikely FH, resulted from DLCC scores of >8, 6–8, 3–5 and 0–2 points, respectively. Patients with DLCC of Definite, Probable and Possible were considered as All FH, while Definite and Probable FH were recognised as Potential FH [2,6]. For those who were on statin and had no baseline LDL-C record, their estimation of untreated LDL-C level was calculated using LDL-c adjustment factor, according to the types and dosage of statin [29]. Patients with baseline LDL-c of <4.0 mmol/L were automatically classified as Unlikely FH.

Biometric data and biological sample collection

Personal and family medical history, smoking status, body mass index (BMI), waist circumference (WC) and lipid-lowering therapy (types and dosage) were collected from the clinics’ database and by on-site measurement. Blood pressure were measured in triplicates after the subjects were seated and rested for 3–5 mins, and the mean of last two readings were regarded as current blood pressure of the subjects [30]. Baseline and current lipid profiles, which include TC, triglycerides (TG), LDL-C and high-density lipoprotein (HDL) were also obtained from the clinics’ database. All medical history, biomarkers and biometric data for PCAD subjects (G1 and G2) were specifically selected at the time point before or during the onset of PCAD. Data for the normal control subjects were obtained through standard questionnaire, assisted by trained research assistants and physician on-site. Their blood samples (9 mL) were collected, and serum lipid profiles were analysed.

Statistical analysis

Data were analysed using IBM SPSS Statistics version 25 (IBM, NY, USA). Continuous data were presented as means (SD) (for parametric test) or median and interquartile range (IQR) (for non-parametric tests), while categorical data were presented as percentages. The significance of differences between the numerical variables was determined by using two-sample t-test and One-Way ANOVA (for parametric tests) or Mann-Whitney and Kruskal Wallis tests (for non-parametric tests). The significance of association between categorical variables was determined by using Chi-squared or Fisher Exact test [31]. Logistic regression was used to describe the association between CRFs and CAD, where variables subjected to univariate analysis with p value <0.25 were included for multiple logistic regression analysis. All final analyses with p value <0.05 were considered as statistically significant.

Ethical consideration and written informed consent

Ethical approval was obtained from participating organisations through the respective Institutional Research Ethics Committees [ref: 600-RMI (5/1/6) and IJNEC/03/2012 (6)] prior to commencement of the study. The study was conducted in accordance with the Declaration of Helsinki. All subjects provided written informed consent prior to enrolment into the study.

Results

Prevalence of familial hypercholesterolaemia

Out of 319 AP-PCAD patients in this study (G1 and G2), the prevalence of clinically diagnosed Definite FH and Probable FH was 6% (19/319) and 10% (32/319), respectively, which add up into 16% Potential FH. When Possible FH was included on top of Potential FH, All FH was 45.5% (145/319). Almost 30% of the AP-PCAD patients (94/319) were Possible FH and 54.5% (174/319) were Unlikely FH ().

Clinical characteristics and risk factors of study population

shows the distribution of participants into groups based on the presence of PCAD and clinical diagnosis of FH according to the DLCC criteria. Malay was the major ethnic across all groups. G1 and G2 (those with PCAD) has higher proportions of obesity (BMI) compared to G3 and G4. The presence of family history of PCAD was also significantly higher in G1 compared to the other groups (p<0.05). Similarly, there was also a significantly high percentage of individuals with family history of HC in G1 compared to G2, G3 and G4 (p<0.05).

TC levels of G1 was significantly higher compared to G2 but significantly lower compared to G3. Pre-treatment LDL-C levels of G1 was significantly higher than G2 and G4, but similar to G3, whom were also FH individuals. Only four patients (3.7%) were presented with TX and 15 patients (13.8%) with corneal arcus in G1. Only one individual (2.0%) presented with TX and 42 individuals (82.4%) with corneal arcus in G3 (which were also FH).

The most common risk factors among individuals with premature coronary artery disease

Individuals with PCAD from G1 (n = 145) and G2 (n = 174) were combined to determine the most common risk factors among PCAD individuals. shows that after HC (98.1%) and male gender (85.3%), obesity was the most common risk factor among this cohort with (77.0%) followed by hypertension (76.5%) and diabetes (51.1%).

Conventional risk factors among individuals with PCAD (n = 319).

Data presented as percentage (%); HC: Hypercholesterolaemia, PCAD: Premature coronary artery disease.

Association between coronary risk factors and premature coronary artery disease

The factors associated with PCAD among all groups in the study was determined using logistic regression analysis (). All risk factors including lipid profiles were analysed for simple logistic regression. Patients without available data were excluded from the analysis. Further multiple logistic regression analysis shows that only FH, Potential and Probable FH, hypertension, diabetes and family history of HC were significantly associated with PCAD among all individuals in the study (p<0.05).

Subjects of G1 (n = 145) and G3 (n = 101) was combined (n = 246) to identify the risk factors associated with PCAD among FH individuals using logistic regression (). All risk factors and lipid profiles were analysed using simple logistic regression. Further multiple logistic regression analysis shows that only Definite FH, hypertension, diabetes mellitus and family history of PCAD were significant risk factors for PCAD among those with FH (p<0.05).

Those with Potential FH (Definite: n = 22; Probable: n = 41) from G1 and G3 was combined to identify the risk factors associated with PCAD among Potential FH using logistic regression (). Due to small data, not all risk factors were able to run through regression analysis. Therefore, only available risk factors were analysed using simple logistic regression. Further multiple logistic regression analysis shows that only obesity and family history of PCAD were significant risk factors for PCAD among Potential FH (p<0.05).

Independent predictors of premature coronary artery disease

The risk factors that were shown to significantly associated with PCAD were re-analysed using the same regression method. The remaining risk factors were then tested for their interactions and all the factors were re-analysed for the final prediction model of PCAD among all individuals in the study . Overall, hypertension and diabetes were the greatest independent risk factors that contributed to development PCAD [OR (95%CI): 14.1 (7.81, 25.61) and OR (95%CI): 4.7 (2.91, 7.73), respectively], followed by clinically diagnosed as FH and Potential FH [OR (95%CI): 2.9 (1.51, 5.46) and OR (95%CI): 4.5 (2.13, 9.63), respectively].

The final prediction model of PCAD among individuals with FH were shown in . Results show that FH patients with diabetes have 6.4 times the odds to develop PCAD compared to those without diabetes. Hypertensive patients were 3.6 times more likely to develop PCAD compared to FH patients without hypertension. Besides, those with family history of PCAD have almost 3.0 times the odds to develop PCAD compared to those without family history of PCAD. Those with Definite FH category have 7.1 times the odds to develop PCAD compared to those who were Probable and Possible FH.

The final prediction model of PCAD among individuals with Potential FH were shown in . Results show that Potential FH patients with family history of PCAD have 6.7 times the odds to develop PCAD compared to those who were not Potential FH. Besides, patients who were obese were 15.4 times more likely to develop PCAD compared to FH patients who were not obese.

Discussion

This study is the first to describe the prevalence of clinically diagnosed FH among PCAD cohort in Malaysia, where the prevalence of 16.0% Potential FH is far higher than in the normal population, which had been reported to be about 1% [5]. FH is generally under-diagnosed globally both in the community as well as in hospital settings [21,32], especially in Asian countries. The strength of this present study was that the status of CAD among subjects in G1 and G2 were confirmed by documented angiogram proven PCAD and/or history of angioplasty and/or bypass surgery rather than by self-reporting questionnaire. Besides, physical examinations of TX and corneal arcus were performed by the doctors and physicians during consultation sessions, in contrast to other studies where physical examination of lipid stigmata were not performed for the diagnosis of FH [33].

Currently, the majority of Malaysian FH patients are not genetically confirmed, where genetic testing itself is still not a standard protocol for FH confirmation in the majority of the Asian countries, including Malaysia [34]. Nevertheless, without the molecular testing, the findings show almost half of the AP-PCAD patients were clinically FH.

To the best of our knowledge, this study is also the first report on the prevalence of FH among angiogram-proven PCAD patients in Southeast Asia, although similar studies, also using DLCC criteria had been previously reported in other regions. While the overall prevalence of FH in this present study is relatively high (45.5%), several studies [32,35] had just reported a similar prevalence of Potential FH (14% - 15%) to this present study where about one third (15%) of AP-PCAD patients were Potential FH and two thirds (30%) were Possible FH.

The prevalence of FH were discovered in lower frequency in several studies, such as a recent study conducted in the United States reported that clinical FH was present in just about 1 out of 10 patients with PCAD (among those with ST-segment elevation myocardial infarction) [36], In contrary, a study in Italy had estimated the prevalence of clinically diagnosed overall FH (including Possible FH) to be very high at 60.4%, with 10.4% of them being Potential FH [37]. Interestingly, the age of onset of PCAD in the latter study was similar to this present study, with patients admitted to cardiac rehabilitation and secondary prevention centres including those with angiogram-proven CAD. Meanwhile, a multi-cohort study in Switzerland with same PCAD age of onset cut-off with this present study, reported the FH prevalence in patients with PCAD as 51.9%, but without definitive angiogram confirmation for PCAD, the proportion of Potential FH is much lower at 4.8% (vs 16.0% in this present study) [38]. This suggests that the detection rate of Potential FH is much higher in those with AP-PCAD or those who had undergone revascularisation procedures, compared to those with ACS without prior angiogram confirmation.

In contrast, a Spanish study reported a much higher prevalence of clinically diagnosed FH among patients with ACS at 77.6%; although with a similar 1:2 ratio between Potential and Possible FH (27.2% of Potential FH and 50.4% Possible FH) [39]. This is possibly due to the higher age cut-off of PCAD used in this study (≤65 years), compared to the present study. Furthermore, this study also determined the prevalence of genetically confirmed FH which was about one-tenth (8.7%) of positive FH rate when clinical diagnosis alone was applied. Our present study did not perform any genetic testing as it not government-funded nor a routine protocol in most Asian countries. Hence, based on the detection rate of the above-mentioned study, the prevalence of genetically confirmed FH among PCAD in this present study could be predicted at approximately 4.6% (one-tenth of 45.5%). However, our study examined for FH in those with AP-PCAD with lower age cut-offs according to DLCN, whilst the Spanish data included all ACS patients with higher age cut-off of ≤65years. Furthermore, the genetic make-up and influence of other modifiable CRFs to development of PCAD may be different between populations. Therefore, future studies are warranted on prevalence of genetically confirmed FH among the AP-PCAD cohort, and the influence of modifiable CRFs to PCAD in these patients in the Asian population.

To date, FH among PCAD patients in hospital settings are still under-diagnosed [40-42]. However, coronary care setting is a useful environment for detecting patients likely to have FH, who should then be referred to specialist service for confirmation of the diagnosis and family cascade screening. Actively diagnosed angiogram-confirmed stenosis is a helpful information in identifying FH among younger PCAD patients, where common physical manifestation of FH such as lipid stigmata is still not identifiable.

Coronary artery disease is a major concern worldwide and CVD is the leading cause of death [43]. Conventional risk factors continue to play a pivotal role in Malaysia. This present study showed that almost 80% PCAD patients with FH (G1) were obese or centrally obese. Previous studies had shown that over 80% of patients with CAD were overweight or obese [44], which was also reflected in this present study, where individuals with AP-PCAD (G1 and G2) had higher combined proportions of overweight and obese individuals when compared to those without CAD (G3 and G4), where the differences were about 10%. This is a concerning finding, where it suggested that FH-CAD group has very suboptimal lifestyle practice that led to obesity, further exacerbating the already-existing high coronary risk within these patients. On the other hand, G3, despite being diagnosed with FH, had a relatively lower proportion overweight and obesity when compared to G1 and G2, could be explained by the G3 being more health conscious with healthier lifestyle after being diagnosed as FH. Furthermore, this present study clearly showed that obesity is an independent predictor of PCAD in potential FH (p<0.002), suggesting that obesity has an impactful influence on development of PCAD in patients with Potential FH.

In this present study, the level of TC among those with FH-PCAD was significantly lower than G3 (FH without CAD). This could be explained by the fact that most individuals with PCAD were already treated with medications for CAD and statin therapy for at least 3 months prior to the data collection.

There was no positive association between smoking status and type of groups in this present study. The regression analysis of factors associated with CAD also showed that smoking was not associated with PCAD, in contrast with other previous studies [45-47]. However, if ex-smokers are combined with current smokers, the number of this combined categories is significantly higher in subjects in group of interest (G1) compared to G2, G3 and G4 (62.1% vs 31.6% vs 56.2% vs 54.3%: respectively). The high number of ex-smokers in G1 was probably due to the fact that many of those with PCAD ceased smoking upon being diagnosed as PCAD. Ex-smokers may still have residual coronary risk, as reported by a previous study, that 10–15 years of quitting smoking is required before the enhanced smoking-related risk subside, compared to non-smokers [48]. This is another concerning issue in public health as the prevalence of smoking (current and ex-smokers) are very high amongst FH-PCAD patients who are already in the very high-risk category.

Traditional predictors of CAD risk which includes age, gender, smoking, hypertension, diabetes mellitus, family history of CAD, personal history of CAD, HDL-C, TG, small dense LDLs, and lipoprotein(a) [Lp(a)] levels have been studied and reported as potential predictors of atherosclerotic burden and/or CVD prognosis among individuals with FH [49,50].

This present study has demonstrated that among all subjects, diabetes, hypertension, FH and Potential FH were independent predictors for PCAD. In subjects with FH, besides diabetes (OR 6.4) and hypertension (OR 3.6), family history of PCAD (OR 3.0) and having clinically diagnosed as Definite FH (OR 7.1) were the independent predictors for PCAD. This is in agreement with previous reports indicating that family history of CAD in first-degree or second-degree relatives and a personal history of CAD in individuals with FH may cause a higher CVD risk [49]. Besides, those with FH and family history of PCAD were at much higher risk to develop PCAD due to their genetic predisposition to lifelong exposure to elevated LDL-C levels. These findings should alert the clinicians when treating FH patients, who are at even greater CAD risk with the presence of positive family history of PCAD, Definite FH, particularly if they have hypertension and diabetes as co-morbidities. In addition, regression analysis among those with Potential FH revealed that family history of PCAD and obesity were significant predictors of PCAD among this high-risk cohort. It is interesting to note that among those with Potential FH, presence of family history of PCAD and obesity, increase the risk of PCAD by 6.7 and 15.4-fold. Obesity is a well-known risk factor for CVD and is also an important factor in hyperlipidaemias that contributes to lipid phenotypes [51]. In the presence of Potential FH, only obesity and family history of PCAD, but not the other CRFs were clearly shown to be independent predictors of PCAD, suggesting strong synergistic effect of these two CRFs with lifelong exposure to HC in predicting PCAD.

The latest guidelines for the management of dyslipidaemias shows that patients who are clinically diagnosed as FH are automatically categorised into the high-risk category if other risk factors are not present [52]. The risk category is further promoted to very high-risk if ASCVD or one other major risk factor is present. The risk categorisation is important as to determine the preventive actions based on the patient’s total CV risk. The higher the risk, the more intense the medication prescription or treatment and the lower the target LDL-C level. The G1 group in this present study is automatically classified as very high risk due to the inherent presence of PCAD with clinically diagnosed FH. In addition to having FH, they have high proportions of other risk factors such as obesity (BMI≥25), central obesity, diabetes and hypertension. The PCAD risk in these patients are postulated to be further enhanced, in the presence of other risk factors especially hypertension, diabetes, obesity and family history of PCAD. Thus, the treatment and management of FH should be tailored towards those in the very high-risk category, in the presence of ASCVD and/or co-existing CRFs, where management of these co-existing CRFs need to be more intense and optimised. However, the other confounding factors such as the nature of disease-causing genetic variants of FH candidate genes and lipoprotein(a) and their contribution to PCAD have not been included in this present study. Therefore, future studies are warranted to address these issues.

Nevertheless, this study had some limitations where it was not possible to validate all information regarding family history, and physical examination for TX and corneal arcus except those performed in the Specialist Lipid Clinics. Therefore, the prevalence of PCAD patients with FH might be underestimated in this study. This study was also not able to fully describe the prevalence of central obesity among PCAD patients without FH (G2), as the waist circumference data were not available in this cohort. However, the regression analysis of independent predictors for PCAD among FH individuals were not affected. Best efforts had been applied to ensure patients in G3 and G4 were free from CAD during the recruitment. However, because the history of CAD for these groups were collected by means self-reporting questionnaire, patients with asymptomatic cardiac disease such as silent cardiac ischaemia may be wrongly classified, thus affecting the regression analysis. Besides, for these patients (G3/G4), the data of risk factors and other characteristics including the fasting serum lipid results were collected at the entry of study, instead of at the onset of CAD like patients in G1 and G2. For some patients in G1/G2, the history of onset of CAD were collected backdatedly, which explains the age range.

Next, this study population of PCAD patients although recruited from a few major Cardiology and Specialist Lipid Clinics, may not be representative of the national data. This present study also did not include genetic testing for molecular confirmation of FH. High proportion of Possible FH in this study was probably contributed by the active physical examination of corneal arcus. However, only a small portion of these Possible FH subjects were probably genetically true FH, if all of them were genetically tested, as what has been demonstrated in a previous study among clinically diagnosed FH patients in an England lipid clinic, where only 28% of Possible FH patients has genetic mutation in FH-associated genes [53].

Conclusion

Clinically diagnosed Potential FH is common among AP-PCAD patients in an Asian population. FH-PCAD subjects have high proportions of the various modifiable risk factors. In the FH patients, diabetes, hypertension, obesity, family history of PCAD and Definite FH are independent predictors of PCAD. FH with CAD are in very-high-risk category, hence, early aggressive treatment and management of modifiable CRFs in these patients are warranted for PCAD prevention.

Early identification of these risk factors among FH patients is important in initiating timely intervention. Given that PCAD prevalence is increasing, and more than half of FH worldwide reside in the Asia-Pacific region, early detection, optimal treatment and PCAD prevention are critically needed, especially in Asia. Genetic testing on PCAD patients may also prompt initiation of family cascade screening, thus promoting early lipid-lowering therapy among affected family members. However, other confounding factors such as the nature of disease-causing genetic variants of FH candidate genes and lipoprotein(a) and their contribution to PCAD have not been included in this present study. Therefore, these issues need to be addressed in future studies. Future research with inclusion of genetic data is warranted to provide more information on the nature of the disease-causing variants of FH-candidate genes, and their association with the other CRFs in predicting PCAD in FH patients.

12 Jan 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Katriina Aalto-Setala, Professor

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files.

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

"Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Nawawi et al. conducted a comparative cross-sectional study to examine the prevalence of familial hypercholesterolemia (FH) among angiogram-proven premature CAD (PCAD) patients, the distribution of coronary risk factors, and the prediction of PCAD by the risk factors in Malaysia. The major findings showed that hypertension [OR (95% CI): 14.1 (7.8, 25.6)] and diabetes [OR (95% CI): 4.7 (2.9, 7.7)] carry much more risk than FH [OR (95% CI): 2.9 (1.5, 5.5)] to predict PCAD. However, such an interpretation needs cautions. Other findings add minimal information to current knowledge regarding FH in PCAD, when lacing genetic analysis.

Major critiques:

1. The importance of possible FH is overemphasized. From the analysis of table 5, the odds ratio for potential FH (definite FH + probable FH) is 4.5, and that for FH (definite FH + probable FH + possible FH) is 2.9. This indicates that possible FH is of minimal clinical significance for predicting PCAD. The author should analyze the odds ratio of possible HF, and if it is the case, the part of possible FH should be toned down in the full paper, including the abstract.

2. The definition of premature CAD (PCAD) in this study is age of onset: males: <55; females: <60 years. However, in Table 1, the age range of Group 3 (+FH/-PCAD) is 42 – 74 years and that for Group 4 (–FH/-PCAD) is 46 – 72 years. The age distribution cannot exclude that some male patients younger than 55 years and female patients younger than 60 years may develop PCAD later in their life and should not grouped as -PCAD.

Other comments

1. The definition of PCAD in this study is age of onset: males: <55; females: <60 years. However, in Table 1, the age range of Group 1 (+FH/+PCAD) is 26 – 76 years and that for Group (–FH/+PCAD) is 35 – 75 years. In this case, presence of cardiovascular risk factors should be before the diagnosis of the CAD to be the risk factors. The authors need to clarify the time sequence of appearance of risk factors and onset of PCAD.

2. Similarly, in Figure 2, regarding the percentage of ex-smoker and current smokers, do you count it at the entry of this study, or at the onset of PCAD?

3. In Group 2, 97.7% of patients were given lipid-lowering drugs, however, the difference of LDL-C values between pre-treatment and post-treatment is 0.4 mmol/L, are the data correct? Because 97.2% of patients in Group 1 were on lipid-lowering drugs and the difference of LDL-C values between pre-treatment and post-treatment is 1.8 mmol/L.

Reviewer #2: Review PONE-D-21-27710

Title: Familial Hypercholesterolaemia and coronary risk factors among patients with angiogram-proven premature coronary artery disease in an Asian cohort

Authors: Nazli SA, Chua Y-A, Kasim NAM et al

Background - This paper reports the prevalence of clinically diagnosed FH in Malaysian patients with and without angiographically proven premature coronary artery disease (AP-PCAD) and, in a four-way analysis, assesses the prevalence of coronary risk factors (CRFs) in subjects with and without AP-PCAD by FH status. It concludes by reporting the results of a multiple logistic regression analysis to determine the association between CRFs and CAD in the total study population and separately for those with FH.

Diagnostic criteria - A particular strength of this large study of 572 subjects is its rigorous evaluation of CAD defined as a previous medical history of an abnormal coronary angiogram with stenosis >50% in at least one major epicardial coronary artery or prior PCI and/or CABG in males with an age of onset <55 & females <60 years. FH was diagnosed using the Dutch Lipid Clinic Network criteria (DLCNC), although the authors accept the lack of confirmatory genetic testing for FH-causing mutations is a limitation. Nevertheless, arguably excessive credence is given in the paper to the DLCNC as a basis for a clinical diagnosis of FH. The criteria were initially developed as a means of cost-effectively maximising detection rates for cascade testing programmes by screening only relatives of index patients at high likelihood of genetic FH based on their DLNC score. Identification of an elevated LDL-cholesterol levels in 1st and 2nd-degree relatives would then confirm the likelihood of FH in the index case as well as identifying previously undiagnosed and untreated relatives. As fast-throughput, less expensive, methods of mutation testing became available, DLCN criteria scores have increasingly been used to select patients warranting confirmatory genetic testing. Detection of an FH-causing mutation then initiates cascade testing for that mutation.

Misclassification with DLCN scores - FH mutation detection rates in patients assessed by DLCN scoring has been documented in a number of studies (e.g. Tada H et al. Circulation Journal 2021;85:891-7, & Futema M et al. Atherosclerosis 2013;229:161-8). The former study included cascade-screened patients and those with Achilles tendon thickness measurements, which may have resulted in a positive diagnostic selection bias, whereas the latter smaller study recruited patients sequentially attending a lipid clinic over a three-year period so there may be less risk of bias. The results are summarised below:

---------------------------------------------------------------------------------------------

Tada H et al Futema M et al

DLCN score n Mutation n Mutation

Diagnosis positive (%) positive (%)

----------------------------------------------------------------------------------------------

Unlikely <3 367 5 (1) 13 3 (23)

Possible 3-5 156 49 (31) 69 19 (28)

Probable 6-8 57 30 (53) 49 19 (39)

Definite. >8 100 91 (91) 89 48 (54)

----------------------------------------------------------------------------------------------

Clearly nearly all patients with a score of <3 will not have an FH-causing mutation and over two thirds with a “possible” score of 3-5 will also be unaffected. A mutation will be identified in only about a half with a “probable” score of 6-8, but the majority with a “definite” score of >8 will have an FH-causing mutation. This suggests that the authors’ conclusion in the abstract that “almost half of the AP-PCAD patients with a score of >3 should be classified as “clinically diagnosed FH” is misleading, particularly since “possible FH” (score 3-5) accounted for 65% (94/145) of all patients categorised as having “clinically diagnosed FH”. In fact, their data shows that only 19/319 (6.0%) have a “definite” score >8 indicating a high likelihood of an FH-causing mutation being identified. They might alternatively consider concluding that 16% have “potential” FH (i.e. DLCN score >6) which suggests that about half of such patients may actually have a mutation. However, it is clearly misleading to conclude in the abstract that the prevalence of FH among AP-PCAD patients is 45.5%. This figure should be relegated to the results section of the paper and removed from the abstract. I accept it is appropriate to consider the finding in relation to the existing literature in the discussion, but it is inappropriate to give it such prominence in the abstract.

Results – These are clearly presented both graphically and in tabular form. In Figure 1, to avoid any confusion, it would be helpful to state in the title that the prevalence of FH is based on the DLCN criteria.

The high prevalence (Table 1) of diabetes in patients with PCAD with and without FH defined by a DLCN score >3 (44% & 57% DM respectively) is striking. Diabetes is, of course, well recognised to result in premature cardiovascular disease. A clinical history of premature CAD scores 2 in the DLCN criteria and patients with premature cerebral or peripheral vascular disease score 1. Consequently, any patient with both premature CAD and cerebrovascular disease will score 3 and be classified as “possible FH” regardless of their LDL cholesterol concentration.

Unfortunately, the presentation of the results at present does not allow the reader to assess whether a diagnosis of diabetes is inflating the DLCN estimate of FH. Although type 2 diabetes is usually associated with raised triglyceride levels, low HDL, and with little or no increase in total and LDL cholesterol, nevertheless, given the mean age of the population studied, it would not be surprising to find that many of the PCAD subjects with diabetes had modestly increased pre-treatment LDL concentrations of >4.0 – 4.9 mmol/l, which would result in a DLCN score of 3 for subjects with PCAD even in the absence of any other clinical criteria indicative of FH. Perhaps the lipid profiles for these patients could be added as a supplementary table using the same format as Table 1? It would be particularly helpful to view the triglyceride concentrations since individuals with the lowest TG level (0.4-1.0 mmol/l) were shown to have the highest mutation detection rate (60%) in the study by Marta et al. (op cit) and decreased to 20% in those in the top quartile (2.16-4.3 mmol/l). Consequently, in the Welsh Cascade Screening Programme, a DLCN score is reduced by 2 points for a fasting TG of 2.5-3.5, and more for higher TG concentrations

Tables 2 & 3 depict clearly the factors associated with PCAD in a logistic regression analysis. I would suggest adding to the title of Table 3 with “clinically diagnosed FH with DLCN score >3 and, similarly, for Table 4 add “with a DLCN score >6”. Table 5 very succinctly summarises the findings of the final prediction model. Again, I would be inclined to include the DLNC scores.

Specific points:

Line 60: The prevalence cited of ~1 in 100 is almost high enough to suggest a “founder effect” but the reference is to a secondary source. Could the authors please cite the primary source?

Line 90: It might be helpful to cite a reference for Malaysia having the lowest mean age of onset for PCAD.

Line 117: Inclusion criteria – presumably both type 1 & 2 patients with diabetes were eligible for inclusion? The tables do not provide these data, but it is possible that this information was not available.

Line 125: A total of 572 patients were recruited to the study. Do the authors know how many patients were eligible, the number approached to participate and the participation rate?

Line 129: The inclusion criteria for AP-PCAD are clearly defined, but the criteria for Non-PCAD “controls” do not appear to be specified. Had the “controls” undergone angiography and been shown to have no evidence of stenosis or was this established by questionnaire? It is important to clarify this.

Line 132: Were lipids and lipoproteins measured centrally or by multiple methods locally at hospitals/clinics that referred patients to the National Heart Institute and Specialist Clinics. Were triglyceride measurements fasting? It might be helpful to be rather more specific about the sampling frame from which patients were recruited.

Line 133: Presumably LDL-C concentrations were calculated using the Friedewald formula?

Line 202: The abbreviation CA for corneal arcus is not commonly used. Was it used earlier in the paper?

References – Reference 24 duplicates number 21

Discussion – The discussion is well written and clearly expressed. I wonder, however, whether it would benefit by shortening? I could not find a word count but my impression is the paper probably exceeds 5,000 words after excluding the references. If the discussion was less discursive, it would probably be more impactful – and it deserves to be.

Lines 262-270 start by justifiably focusing on the main findings and the strengths of the study. Nevertheless, I think it is a mistake to assert, as the headline finding, that the prevalence of FH was 45.5% among AP-PCAD patients. Instead, I suggest using as a surrogate for “clinically diagnosed FH” a DLCN score of either >6 [probable + definite FH termed “potential” FH in the paper] or >8 [“definite”- approximating to a genetically confirmed diagnosis]. Including patients with a score of 3-5 [“possible”] introduces diagnostic misclassification and results in over-interpretation of the findings and an unconvincing prevalence estimate of 45.5%. Using this obviously fallible definition undermines the credibility of the study’s findings.

Lines 271-277. Most health care systems do not have access to routine genetic testing for FH. Perhaps one sentence might suffice for this paragraph.

Lines 278-287. This paragraph places the findings in the context of the existing literature and is important. The next paragraph (lines 288-299) suggests that the detection rate of potential FH (score >6) is higher in patients with AP-PCAD, or those who have undergone revascularisation procedures, when compared to those with ACS without prior angiographic confirmation. Perhaps simply citing references here rather than detailing individual studies would suffice?

Lines 300-320. The key fact here seems to be that in this Spanish study (ref 43) the prevalence of genetically confirmed FH in patients with PCAD was not dissimilar to that in the current study using the DLNC score for “definite FH”. A further Northern European Study (44) also reported similar consistent findings.

Lines 321-328. This paragraph highlights the importance of considering the diagnosis of FH in patients with a diagnosis of PCAD in the coronary care setting and the need for referral to specialist clinics for confirmation of the diagnosis and cascade testing. Perhaps it could be somewhat shortened?

The remainder of the discussion concentrates on the interesting results of the logistic regression analyses that examine the independent predictors for PCAD. These findings are consistent with previous studies. Interestingly, obesity was shown to be a significant independent predictor of PCAD in patients with potential FH (DLNC score >6). Northern European registry studies have low rates of obesity in FH patients and cannot, therefore, address this question with adequate statistical power. However, as their populations are becoming increasingly overweight and obese, this is potentially an important insight. Overall, however, the authors may feel this section of the discussion could be shortened.

Before concluding, the authors consider objectively limitations of the study and, perhaps, the diagnostic misclassification associated particularly with lower DLCN FH scores should be added.

Summary – This is an interesting study that extends existing knowledge by assessing the prevalence of clinically diagnosed FH in Malaysian patients with and without premature coronary artery disease. The categorisation of cases was rigorously defined angiographically, but it is not clear whether unaffected patients had undergone angiography with no evidence of stenosis found or whether this was elicited by questionnaire. FH was defined clinically using the Dutch Lipid Clinic Network score which inevitably, in the absence of mutation testing, results in some diagnostic misclassification. This is most marked in the lowest DLCN diagnostic category of “possible FH” with a score of 3-5 in which no FH-causing mutation will be found in over two thirds of patients. It is therefore very misleading to conclude in the abstract that the prevalence of FH among AP-PCAD is 45.5% based on a score of >3 since “possible” FH accounted for 65% of patients with “clinically diagnosed FH”. The multiple logistic regression analysis results are convincing and interesting findings. Overall, the impact of the paper would arguably be strengthened if the discussion was substantially shortened.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Professor H.Andrew W. Neil

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

28 Apr 2022

Thank you very much for the kind and constructive questions and comments. We really appreciate the consideration and attention. Below are our answers to the reviewers:

# We have shortened the group’s name in the manuscript from Group 1 to G1, Group 2 to G2, Group 3 to G3 and Group 4 to G4.

Reviewer 1:

Comment:

Nawawi et al. conducted a comparative cross-sectional study to examine the prevalence of familial hypercholesterolemia (FH) among angiogram-proven premature CAD (PCAD) patients, the distribution of coronary risk factors, and the prediction of PCAD by the risk factors in Malaysia. The major findings showed that hypertension [OR (95%CI): 14.1 (7.8, 25.6)] and diabetes [OR (95% CI): 4.7 (2.9, 7.7)] carry much more risk than FH [OR (95% CI): 2.9 (1.5, 5.5)] to predict PCAD. However, such an interpretation needs cautions. Other findings add minimal information to current knowledge regarding FH in PCAD, when lacing genetic analysis.

Response:

Some studies had demonstrated that hypertension and diabetes can be a much greater independent risk for coronary artery disease even when compared with lipid biomarkers such as LDL-C level (Chan et al., 2015), which is supposedly the main indicator of FH.

However, we agree to tone down the finding of hypertension and diabetes by rephrasing the Result section. (Page 22, turquoise highlight)

Reference: Chan DC, Pang J, Hooper AJ, Burnett JR, Bell DA, Bates TR, van Bockxmeer FM, Watts GF. Elevated lipoprotein (a), hypertension and renal insufficiency as predictors of coronary artery disease in patients with genetically confirmed heterozygous familial hypercholesterolemia. International journal of cardiology. 2015 Dec 15;201:633-8.

Comment:

The importance of possible FH is overemphasized. From the analysis of table 5, the odds ratio for potential FH (definite FH + probable FH) is 4.5, and that for FH (definite FH + probable FH + possible FH) is 2.9. This indicates that possible FH is of minimal clinical significance for predicting PCAD. The author should analyze the odds ratio of possible HF, and if it is the case, the part of possible FH should be toned down in the full paper, including the abstract.

Response:

The independent odds ratio for Possible FH has been calculated in Table 2 and 3, where it turns out that regardless of what is the sample cohort (whether among all subjects or just among FH), the odds ratios for association of Possible FH with PCAD is <1, which means Possible FH has minimal clinical significance for predicting FH. We have toned down the prevalence of All FH by relegate it to the result section only, and further highlight the importance of Potential FH.

Abstract: Page 2-3, turquoise highlight.

Results: Page 10, turquoise highlight.

Discussion: Page 23-24, turquoise highlight.

Conclusion: Page 29, turquoise highlight.

Comment:

The definition of premature CAD (PCAD) in this study is age of onset: males: <55; females: <60 years. However, in Table 1, the age range of Group 3 (+FH/-PCAD) is 42 – 74 years and that for Group 4 (–FH/-PCAD) is 46 – 72 years. The age distribution cannot exclude that some male patients younger than 55 years and female patients younger than 60 years may develop PCAD later in their life and should not grouped as -PCAD.

Response:

The definition of PCAD used in this study was applied to patients in G1 (+FH/+PCAD) and G2 (-FH/+PCAD); in which these patients were recruited cross-sectionally based on retrospective diagnosis of CAD, and more importantly, the CAD is already developed and confirmed by angiography.

For patients in G3 and G4 (-PCAD), we do not deny that the subjects may develop CAD in younger age (as young as 42 years old in G3, and as young as 46 years in G4). However, we deliberately match the age among G1, G2, G3 and G4 so their ages were not significantly different in order to ensure age-sensitive variables such as blood pressure and corneal arcus will have equal odds across the groups. Nevertheless, we have explained the possibility of recruiting patients with silent asymptomatic ischaemia in G3 & G4 in Discussion section. (Discussion: Page 28, turquoise highlight.)

Comment:

The definition of PCAD in this study is age of onset: males: <55; females: <60 years. However, in Table 1, the age range of Group 1 (+FH/+PCAD) is 26 – 76 years and that for Group (–FH/+PCAD) is 35 – 75 years. In this case, presence of cardiovascular risk factors should be before the diagnosis of the CAD to be the risk factors. The authors need to clarify the time sequence of appearance of risk factors and onset of PCAD.

Response:

The PCAD onset age is already explained the “Methodology - Study design and patient recruitment” section. Care has been taken in G1 (+FH/+PCAD) and G2 (-FH/+PCAD) where only coronary risk factors that appeared before or during the onset of PCAD were recorded. Additional phrase regarding the sequence of appearance of risk factors has been added in “Methodology - Biometric data and biological sample collection” section (Page 8, turquoise highlight).

Comment:

Similarly, in Figure 2, regarding the percentage of ex-smoker and current smokers, do you count it at the entry of this study, or at the onset of PCAD?

Response:

The smoking status were specifically recorded at the onset of PCAD, which should be already sufficiently explained in the correction at “Methodology - Biometric data and biological sample collection” section. (Methodology: Page 8, turquoise highlight.)

Comment:

In Group 2, 97.7% of patients were given lipid-lowering drugs, however, the difference of LDL-C values between pre-treatment and post-treatment is 0.4 mmol/L, are the data correct? Because 97.2% of patients in Group 1 were on lipid-lowering drugs and the difference of LDL-C values between pre-treatment and post-treatment is 1.8 mmol/L.

Response:

Yes, the data is indeed correct. The post-treatment LDL-C reduction in G1 seems greater than that in G2 because G1 is consists of FH patients, who has inherently high pre-treatment LDL-C (mean = 5.1 mmol/L) compared to the non-FH G2 (mean = 2.4 mmol/L). According to the Malaysian Clinical Practice Guidelines for Dyslipidaemia, the targeted post-treatment LDL-C for very-high coronary risk patients is 1.8 mmol/L. However, according to our preliminary data (Chua et al., 2021 – abstract in conference proceeding), only <10% of FH patients achieved the targeted LDL-C of 1.8 mmol/L, where large portion of patients only achieved post-treatment LDL-C of 2.6 mmol/L or greater. Non-FH hypercholesterolaemic patients may achieve better post-treatment LDL-C level where 23% of them achieved 1.8 mmol/L (Razman et al., 2021 – abstract in conference proceeding).

This explains why the baseline LDL-C of G1 is higher compared to G2, but the post-treatment LDL-C levels of both groups were similar.

Reference:

1) Yung-An Chua, Sukma Azureen Nazli, Azhari Rosman, Sazzli Shahlan Kasim, Khairul Shafiq Ibrahim, Ahmad Bakhtiar Md Radzi, Noor Alicezah Mohd Kasim, Hapizah Mohd Nawawi. Prescription pattern and achievement of LDL-C targets with lipid-lowering medications among familial hypercholesterolaemia patients attending Specialist Clinics in Malaysia. The 19th International Symposium on Atherosclerosis, Kyoto, 2021.

2) Aimi Zafira Razman, Noor Alicezah Mohd Kasim, Zaliha Ismail, Alyaa Al-Khateeb and Hapizah Mohd Nawawi. Sub-optimal prescription of lipid-lowering medications and achievement of LDL-C targets in hypercholesterolaemic individuals in the community in the high and very high cardiovascular risk categories. The 19th International Symposium on Atherosclerosis, Kyoto, 2021.

________________________________________________________________________________________

Reviewer 2

Comment:

Misclassification with DLCN scores - FH mutation detection rates in patients assessed by DLCN scoring has been documented in a number of studies (e.g. Tada H et al. Circulation Journal 2021;85:891-7, & Futema M et al. Atherosclerosis 2013;229:161-8). The former study included cascade-screened patients and those with Achilles tendon thickness measurements, which may have resulted in a positive diagnostic selection bias, whereas the latter smaller study recruited patients sequentially attending a lipid clinic over a three-year period so there may be less risk of bias. The results are summarised below:

-------------------------------------------------------

Tada H et al Futema M et al

DLCN score n Mutation n Mutation

Diagnosis positive (%) positive (%)

-----------------------------------------------------

FH Score Tada H et al Futema M et al

Unlikely < 3 367 5 (1) 13 3 (23)

Possible 3-5 156 49 (31) 69 19 (28)

Probable 6-8 57 30 (53) 49 19 (39)

Definite > 8 100 91 (91) 89 48 (54)

---------------------------------------------------------------

Clearly nearly all patients with a score of <3 will not have an FH-causing mutation and over two thirds with a “possible” score of 3-5 will also be unaffected. A mutation will be identified in only about a half with a “probable” score of 6-8, but the majority with a “definite” score of >8 will have an FH-causing mutation. This suggests that the authors’ conclusion in the abstract that “almost half of the AP-PCAD patients with a score of >3 should be classified as “clinically diagnosed FH” is misleading, particularly since “possible FH” (score 3-5) accounted for 65% (94/145) of all patients categorised as having “clinically diagnosed FH”. In fact, their data shows that only 19/319 (6.0%) have a “definite” score >8 indicating a high likelihood of an FH-causing mutation being identified. They might alternatively consider concluding that 16% have “potential” FH (i.e., DLCN score >6) which suggests that about half of such patients may actually have a mutation. However, it is clearly misleading to conclude in the abstract that the prevalence of FH among AP-PCAD patients is 45.5%. This figure should be relegated to the results section of the paper and removed from the abstract. I accept it is appropriate to consider the finding in relation to the existing literature in the discussion, but it is inappropriate to give it such prominence in the abstract.

Response:

We really appreciate the efforts of Reviewer 2 in thoroughly explained the quoted references for backing up his/her comment. The comment is insightful and fair.

We have reduced the prominence of All FH, highlighted the prevalence of Potential FH, and concluded that Potential FH is clinically more important than All FH in the Abstract and Result sections.

We also revised the Conclusion section, where the prevalence of All FH has been and replaced by mentioning Potential FH is common among Asian population.

However, for comparison purpose, we still mentioned few studies which have similarly high prevalence of All FH in Discussion section.

Abstract: Page 2-3, turquoise highlight.

Results: Page 10, turquoise highlight.

Discussion: Page 23-24, turquoise highlight.

Conclusion: Page 29, turquoise highlight.

Comment:

Results – These are clearly presented both graphically and in tabular form. In Figure 1, to avoid any confusion, it would be helpful to state in the title that the prevalence of FH is based on the DLCN criteria.

- The comment has been addressed.

Comment:

The high prevalence (Table 1) of diabetes in patients with PCAD with and without FH defined by a DLCN score >3 (44% & 57% DM respectively) is striking. Diabetes is, of course, well recognised to result in premature cardiovascular disease. A clinical history of premature CAD scores 2 in the DLCN criteria and patients with premature cerebral or peripheral vascular disease score 1. Consequently, any patient with both premature CAD and cerebrovascular disease will score 3 and be classified as “possible FH” regardless of their LDL cholesterol concentration.

Response:

In DLCN, personal premature CAD and personal peripheral vascular disease are grouped into a same group of criteria (Personal Clinical History). According to the rule for counting the DLCN score, only one criterion with the highest point will be counted for the overall score (Nordestgaard et al., 2013). If a patient is with both premature CAD and peripheral vascular, he/she will score 2 points (premature CAD) instead of 3.

In our study design, we have avoided misclassification of FH patients by only including subjects with baseline LDL-C of >4.0 mmol/L into the DLCN scoring. Those with <4.0 mmol/L were automatically classified as Unlikely FH.

We have included the extra information regarding the inclusion criteria in “Methodology – Definition of Terms” section. (Methodology: Page 8, green highlight.)

Reference:

Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, Descamps OS, Wiklund O, Hegele RA, Raal FJ, & Defesche JC. (2013). Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease. European Heart Journal 34(45): 3478-3490.

Comment:

Unfortunately, the presentation of the results at present does not allow the reader to assess whether a diagnosis of diabetes is inflating the DLCN estimate of FH. Although type 2 diabetes is usually associated with raised triglyceride levels, low HDL, and with little or no increase in total and LDL cholesterol, nevertheless, given the mean age of the population studied, it would not be surprising to find that many of the PCAD subjects with diabetes had modestly increased pre-treatment LDL concentrations of >4.0 – 4.9 mmol/l, which would result in a DLCN score of 3 for subjects with PCAD even in the absence of any other clinical criteria indicative of FH. Perhaps the lipid profiles for these patients could be added as a supplementary table using the same format as Table 1? It would be particularly helpful to view the triglyceride concentrations since individuals with the lowest TG level (0.4-1.0 mmol/l) were shown to have the highest mutation detection rate (60%) in the study by Marta et al. (op cit) and decreased to 20% in those in the top quartile (2.16-4.3 mmol/l). Consequently, in the Welsh Cascade Screening Programme, a DLCN score is reduced by 2 points for a fasting TG of 2.5-3.5, and more for higher TG concentrations .

Response:

The prevalence of diabetes in Malaysia normal population was 18.3% in normal population (Ministry of Health Malaysia, 2020), while in Japan, it is 12.1% (Fujii et al., 2021), where the difference of prevalence between these two countries is about 33%.

Among FH Japanese patients, the prevalence of FH in Japan to 28.6% (Teramoto et al., 2018), which means the Malaysian prevalence of diabetes among FH at 44.1% calculated (about 35% difference) in this current study is plausible.

References:

1. Ministry of Health Malaysia. (2020). National Health and Morbidity Survey 2019 - Non-communicable Disease: Risk Factors and other Health Problems.

2. Fujii H, Funakoshi S, Maeda T, Satoh A, Kawazoe M, Ishida S, Yoshimura C, Yokota S, Tada K, Takahashi K, Ito K. Eating Speed and Incidence of Diabetes in a Japanese General Population: ISSA-CKD. Journal of Clinical Medicine. 2021 Jan;10(9):1949.

3. Teramoto T, Kai T, Ozaki A, Crawford B, Arai H, & Yamashita S. (2018). Treatment patterns and lipid profile in patients with familial hypercholesterolemia in Japan. Journal of Atherosclerosis and Thrombosis 25(7): 580-592.

Comment:

Tables 2 & 3 depict clearly the factors associated with PCAD in a logistic regression analysis. I would suggest adding to the title of Table 3 with “clinically diagnosed FH with DLCN score >3 and, similarly, for Table 4 add “with a DLCN score >6”. Table 5 very succinctly summarises the findings of the final prediction model. Again, I would be inclined to include the DLNC scores.

- The comment has been addressed, thank you very much.

Comment:

Line 60: The prevalence cited of ~1 in 100 is almost high enough to suggest a “founder effect” but the reference is to a secondary source. Could the authors please cite the primary source?

Response:

The comment has been addressed (Introduction: Page 4, turquoise highlight.)

Comment:

Line 90: It might be helpful to cite a reference for Malaysia having the lowest mean age of onset for PCAD.

Response:

The comment has been addressed. The sentence has been slightly changed. (Introduction: Page 5, turquoise highlight.)

Comment:

Line 117: Inclusion criteria – presumably both type 1 & 2 patients with diabetes were eligible for inclusion? The tables do not provide these data, but it is possible that this information was not available.

Response:

Yes, both patients with type 1 & 2 diabetes were included in the study. We have added the description in “Methodology – Definition of terms”. (Methodology: Page 7, turquoise highlight.)

Comment:

Line 125: A total of 572 patients were recruited to the study. Do the authors know how many patients were eligible, the number approached to participate and the participation rate?

Response:

For G1 (+FH/+PCAD) and G2 (-FH/+PCAD), the eligibility of patients are as below:

Eligible patients, n=815

The number approached, n=368

Included (participation rate), n (%) : 319 (86.7)

However, due to the nature of G3 (+FH/-PCAD) and G4 (+FH/-PCAD) which were recruited from convenient sampling among the community, the number of approached subjects were far numerous than what was minimally needed by the statistical analysis. Out of n=4702 non-PCAD subjects, n=253/4702 were age, ethnic and gender-matched and randomly selected for this study.

Comment:

Line 129: The inclusion criteria for AP-PCAD are clearly defined, but the criteria for non-PCAD “controls” do not appear to be specified. Had the “controls” undergone angiography and been shown to have no evidence of stenosis or was this established by questionnaire? It is important to clarify this.

Response:

Due to the study design, it was impossible to access the medical records of the controls who solely consisted of subjects recruited from community health screening programmes, where the programmes were held in municipal public halls instead of established health centres. The information on the CAD status among unaffected G3 (+FH/-PCAD) and G4 (-FH/-PCAD) were obtained by means of assisted self-reporting questionnaire without angiography confirmation.

Comment:

Line 132: Were lipids and lipoproteins measured centrally or by multiple methods locally at hospitals/clinics that referred patients to the National Heart Institute and Specialist Clinics. Were triglyceride measurements fasting? It might be helpful to be rather more specific about the sampling frame from which patients were recruited.

Response:

The lipids and lipoproteins were measured by multiple methods at each hospitals/clinics (National Heart Institute and Specialist Clinics) - Yes, the triglyceride measures at fasting.

Comment:

Line 133: Presumably LDL-C concentrations were calculated using the Friedewald formula?

Response:

Yes. Calculated LDL-C derived from total cholesterol, HDL, and triglycerides levels using Friedewald formula is a standard laboratory protocol in Malaysia. We intentionally did not elaborate the details of the lipid profile analysis in order to shorten the Methodology section.

Comment:

Line 202: The abbreviation CA for corneal arcus is not commonly used. Was it used earlier in the paper?

Response:

The “CA” is not a common abbreviation. We have changed all the “CA” to “corneal arcus” in the manuscript.

Comment:

References – Reference 24 duplicates number 21

Response:

The comment has been addressed. We have removed the duplicated reference.

Comment:

Discussion – The discussion is well written and clearly expressed. I wonder, however, whether it would benefit by shortening? I could not find a word count, but my impression is the paper probably exceeds 5,000 words after excluding the references. If the discussion was less discursive, it would probably be more impactful – and it deserves to be.

Response:

We have shortened the Discussion section slightly, thus making the word count well under 5000 words (From Introduction to Acknowledgement).

Comment:

Lines 262-270 start by justifiably focusing on the main findings and the strengths of the study. Nevertheless, I think it is a mistake to assert, as the headline finding, that the prevalence of FH was 45.5% among AP-PCAD patients. Instead, I suggest using as a surrogate for “clinically diagnosed FH” a DLCN score of either >6 [probable + definite FH termed “potential” FH in the paper] or >8 [“definite”- approximating to a genetically confirmed diagnosis]. Including patients with a score of 3-5 [“possible”] introduces diagnostic misclassification and results in over-interpretation of the findings and an unconvincing prevalence estimate of 45.5%. Using this obviously fallible definition undermines the credibility of the study’s findings.

Response:

The prevalence of FH based on All-FH has been de-emphasised in the Abstract, Results and Conclusion section.

Abstract: Page 2-3, turquoise highlight.

Results: Page 10, turquoise highlight.

Conclusion: Page 29, turquoise highlight.

Comment:

Lines 271-277. Most health care systems do not have access to routine genetic testing for FH. Perhaps one sentence might suffice for this paragraph.

Response:

The said lines have been shortened. (Discussion: Page 23).

Comment:

Lines 278-287. This paragraph places the findings in the context of the existing literature and is important. The next paragraph (lines 288-299) suggests that the detection rate of potential FH (score >6) is higher in patients with AP-PCAD, or those who have undergone revascularisation procedures, when compared to those with ACS without prior angiographic confirmation. Perhaps simply citing references here rather than detailing individual studies would suffice?

Response:

The said lines have been shortened. (Discussion: Page 23, turquoise highlight.)

Comment:

Lines 321-328. This paragraph highlights the importance of considering the diagnosis of FH in patients with a diagnosis of PCAD in the coronary care setting and the need for referral to specialist clinics for confirmation of the diagnosis and cascade testing. Perhaps it could be somewhat shortened?

Response:

The said lines have been shortened. (Discussion: Page 25, turquoise highlight.)

Comment:

The remainder of the discussion concentrates on the interesting results of the logistic regression analyses that examine the independent predictors for PCAD. These findings are consistent with previous studies. Interestingly, obesity was shown to be a significant independent predictor of PCAD in patients with potential FH (DLNC score >6). Northern European registry studies have low rates of obesity in FH patients and cannot, therefore, address this question with adequate statistical power. However, as their populations are becoming increasingly overweight and obese, this is potentially an important insight. Overall, however, the authors may feel this section of the discussion could be shortened.

Response:

The entire paragraph starting with “coronary artery disease is a major concern worldwide…” has been slightly shortened.

Comment:

Before concluding, the authors consider objectively limitations of the study and, perhaps, the diagnostic misclassification associated particularly with lower DLCN FH scores should be added.

Response:

Since our FH subjects were all hypercholesterolaemic (baseline LDL-c <4.0 mmol/L), we are quite confident that the subjects with lower DLCN score were not caused by misinterpretation of LDL-C levels.

However, we fully acknowledge that our Possible FH, which is about 2/3 of all FH patients, is relatively high in proportion and many of the Possible FH subjects may not be molecularly true FH if confirm by genetic testing.

The caveat has been mentioned at the end of Discussion section. (Discussion: Page 29, green highlight.)

Comment:

Summary – This is an interesting study that extends existing knowledge by assessing the prevalence of clinically diagnosed FH in Malaysian patients with and without premature coronary artery disease. The categorisation of cases was rigorously defined angiographically, but it is not clear whether unaffected patients had undergone angiography with no evidence of stenosis found or whether this was elicited by questionnaire.

Response:

As what has been answered for “Line 129” comment, we would like to clarify that the unaffected patients were assumed absent of CAD by means of assisted self-reporting questionnaire, without angiography confirmation.

Comment:

FH was defined clinically using the Dutch Lipid Clinic Network score which inevitably, in the absence of mutation testing, results in some diagnostic misclassification. This is most marked in the lowest DLCN diagnostic category of “possible FH” with a score of 3-5 in which no FH-causing mutation will be found in over two thirds of patients. It is therefore very misleading to conclude in the abstract that the prevalence of FH among AP-PCAD is 45.5% based on a score of >3 since “possible” FH accounted for 65% of patients with “clinically diagnosed FH”.

Response:

As what has been previously commented, we have changed the prevalence of FH by emphasising the Potential FH instead of All FH in the Abstract, Results and Conclusion sections.

Abstract: Page 2-3, turquoise highlight.

Results: Page 10, turquoise highlight.

Conclusion: Page 29, turquoise highlight.

Comment:

The multiple logistic regression analysis results are convincing and interesting findings. Overall, the impact of the paper would arguably be strengthened if the discussion was substantially shortened.

Response:

We have tried our best to shorten the Discussion section. Thank you very much for all the comments.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

25 May 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Katriina Aalto-Setala, Professor

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: It has been a long time since the previous comments were sent (September, 2021). This delay in response and the substantial change of the revised manuscript makes this reviewer to read the revised manuscript from the beginning again and find some critical points.

Major critiques

1. This is a very unique cohort that patients with premature CAD account for more than 75% (145+174/145+174+101=76.0%) of all patients with CAD. Is this cohort representative of CAD population in your country?

2. Methodology, how group 4 patients were recruited was not mentioned.

3. Table 1, data of central obesity for group 2 (n = 174) are missing, which is difficult to understand.

4. The patient number in Table 4 is 63, rather small, and the statistical analysis may not be appropriate.

Other comments

Abstract, 2nd sentences, “Subjects were divided into AP-CAD with FH (G1)…” �  “Subjects were divided into AP-PCAD with FH (G1)…”

Reviewer #2: The authors have addressed my comments. However, I would very strongly recommend one change to the abstract to include the figures for Definite FH. Lines 34-36 should read "The prevalence of Definite, Potential and All FH among AP-PCAD patients were 6%(19/319), 16% (51/319) and 45.5% (145/319) respectively. It is very important to include upfront the figures for definite patients since these are consistent in findings in non-Asian studies and include patients overwhelmingly likely to have an FH-causing variant.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Professor Andrew Neil

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

29 Jun 2022

Reviewer #1:

1. This is a very unique cohort that patients with premature CAD account for more than 75% (145+174/145+174+101=76.0%) of all patients with CAD. Is this cohort representative of CAD population in your country?

= We would like to clarify that only G1 and G2 are consist of PCAD individuals (145+174= 319). The rests in G3 and G4 (101+152= 253) are those without CAD/PCAD. Therefore, this study does not represent the proportions of PCAD among CAD cases in Malaysia.

=We have added extra description in the following section to clarify the nature of G3 and G4.

=Methodology - Study design and patient recruitment: (Page 7, green highlight, Lines 130-132).

2. Methodology, how group 4 patients were recruited was not mentioned.

= Those in group 3 (G3) and 4 (G4) were recruited through community health screening programmes. The methodology was briefly mentioned previously, but now we have added a new paragraph to emphasise the point.

= Methodology - Study design and patient recruitment: (Page 7, yellow highlight, Lines 126-128).

3. Table 1, data of central obesity for group 2 (n = 174) are missing, which is difficult to understand.

= The data of central obesity for G2 (-FH/+PCAD) is missing because we did not take the measurements of waist circumference for those in this group due to the nature of recruitment for this particular group. Although the PCAD patients in G2 were consented for this study, the recruitment began way earlier than G1, G3 and G4, where the measurement of waist circumference was not part of the recruitment protocol.

=For the overall analysis of regression (Results: Table 2) which involves all groups including G2 (n=572), we have no choice but to exclude central obesity.

=However, we are still able to include central obesity as one of the analysis variables in regression analysis that involves FH patients in G1 and G3 (Results: Table 3 and Table 4).

=Nevertheless, the absence of central obesity does not adversely affect the regression analysis and the final outcome of this study.

4. The patient number in Table 4 is 63, rather small, and the statistical analysis may not be appropriate.

= Based on the central limit theorem (CLT), the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the population's distribution. Sample sizes equal to or greater than 30 are often considered sufficient for the CLT to hold (Mascha et al., 2018).

=Besides, all data for n=63 individuals were complete and variables from the univariate analysis with p value <0.25 were included in the multivariate analysis. For multivariate analysis, the value of p <0.05 were taken as significant.

=Therefore, we are quite confident that our sample size for Table 4 is sufficient, and its statistical analysis is appropriate.

=Reference:

Mascha, E. J., & Vetter, T. R. (2018). Significance, errors, power, and sample size: the blocking and tackling of statistics. Anesthesia & Analgesia, 126(2), 691-698.

5. Abstract, 2nd sentences, “Subjects were divided into AP-CAD with FH (G1)…” �  “Subjects were divided into AP-PCAD with FH (G1)…”

= Thank you very much for the comment. We have corrected the term “AP-CAD” to “AP-PCAD”.

= Abstract: (Page 2, green highlight, Line 30).

Reviewer #2:

1. The authors have addressed my comments. However, I would very strongly recommend one change to the abstract to include the figures for Definite FH. Lines 34-36 should read "The prevalence of Definite, Potential and All FH among AP-PCAD patients were 6%(19/319), 16% (51/319) and 45.5% (145/319) respectively. It is very important to include upfront the figures for definite patients since these are consistent in findings in non-Asian studies and include patients overwhelmingly likely to have an FH-causing variant.

= Thank you very much for the recommendation. We have added the prevalence for Definite FH as suggested.

= Abstract: (Page 2, turquoise highlight, Lines 34-36).

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

18 Aug 2022

Familial hypercholesterolaemia and coronary risk factors among patients with angiogram-proven premature coronary artery disease in an Asian cohort

PONE-D-21-27710R2

Dear Dr. NAWAWI,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Xiao-Feng Yang, MD, PhD, FAHA

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This reviewer accepted the responses to my comments from the authors. However, the new information provided in the revised manuscript raises some concerns, as follows:

1. The new information provided in the revised manuscript regarding the recruitment does not include group 3. In the Methodology (lines 119-129), it is clearly described that “The inclusion criteria were male and female Malaysians aged ≥18 years, with AP-PCAD and voluntarily consented to participate in this study…” and that “Normal control subjects were collected through community health screening programmes...” Apparently, groups 1 and 2 are subjects with AP-PCAD, and group 4 is normal control subjects (line 132). However, how subjects of group 3, with FH but without PACD/CAD were recruited is not clear.

2. CAD includes PCAD, but PCAD does not include whole CAD. The new information in the revised manuscript reads that “G3 (Group 3 – Non-PCAD, and non-CAD, but with FH) and G4 (Group 4 - normal controls, without PCAD and CAD, nor FH)…” (lines 131-133). To make it clear to the readers, in Table 1 the “G3 +FH/-PCAD” would be best abbreviated as “G3 +FH/-CAD”. Similarly, in Table 1, “G4 -FH/-PCAD” would be best abbreviated as “G4 -FH/-CAD”.

Reviewer #2: The authors have addressed my comments satisfactorily by including in the abstract the number of subjects with definite familial hypercholesterolaemia

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Professor H.Andrew W Neil

**********

25 Aug 2022

PONE-D-21-27710R2

Familial hypercholesterolaemia and coronary risk factors among patients with angiogram-proven premature coronary artery disease in an Asian cohort

Dear Dr. NAWAWI:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Xiao-Feng Yang

Academic Editor

PLOS ONE

Table 1

Distribution of individuals based on the presence of CAD and clinical diagnosis of FH (n = 572).

Parameters	G1+FH/+PCAD(n = 145)	G2-FH/+PCAD(n = 174)	G3+FH/-CAD(n = 101)	G4-FH/-CAD(n = 152)	p value
Gender Male Female	128 (88.3)17 (11.7)	144 (82.8)30 (17.2)	89 (88.1)12 (11.9)	133 (87.5)19 (12.5)	NS^
Age (years)	54.3 ± 9.3	56.8 ± 9.1	55.4 ± 8.5	55.5 ± 6.8	NS@
Age range (years)	26–76	35–75	42–74	46–72	-
Ethnicity Malay Chinese Indian & Others	107 (73.8)15 (10.3)23 (15.9)	129 (74.1)26 (14.9)19 (10.9)	91 (90.1)2 (2.0)8 (7.9)	113 (74.3)15 (9.9)24 (15.8)	0.007^
BMI# Underweight Normal Overweight Obese	0 (0.0)14 (9.9)15 (10.6)112 (79.4)	0 (0.0)7 (7.1)19 (19.4)72 (73.5)	3 (3.1)17 (17.3)12 (12.2)66 (67.4)	1 (0.2)32 (21.5)31 (20.8)85 (57.0)	<0.001^
Central Obesity# Yes No	85 (77.3)25 (22.7)	† †	58 (59.2)40 (40.8)	97 (65.1)52 (34.9)	0.037^
Smoker# Current smoker Ex-smoker Non-smoker	40 (27.6)50 (34.5)55 (37.9)	28 (16.1)27 (15.5)119 (68.4)	25 (26.0)29 (30.2)42 (43.8)	44 (29.1)38 (25.2)69 (45.7)	<0.001^
Diabetes Yes No	64 (44.1)81 (55.9)	99 (56.9)75 (43.1)	9 (9.1)90 (90.9)	25 (16.4)127 (83.6)	<0.001^
Hypertension Yes No	93 (64.1)52 (35.9)	151 (86.8)23 (13.2)	28 (28.3)71 (71.7)	39 (25.7)113 (74.3)	<0.001^
Hypercholesterolaemia Yes No	142 (97.9)3 (2.1)	171 (98.3)3 (1.7)	31 (31.3)68 (68.7)	27 (17.8)125 (82.2)	<0.001^
On lipid lowering therapy# Yes No	141 (97.2)4 (1.8)	170 (97.7)4 (2.3)	24 (24.5)74 (75.5)	11 (7.5)136 (92.5)	<0.001^
Family history of PCAD# Yes No	55 (37.9)90 (62.1)	9 (5.2)165 (94.8)	14 (15.4)77 (84.6)	17 (11.7)128 (88.3)	<0.001^
Family history of HC# Yes No	49 (33.8)96 (66.2)	1 (0.6)173 (99.4)	14 (15.2)78 (84.8)	11 (7.6)134 (92.4)	<0.001^
Tendon xanthomata# Yes No	4 (3.7)105 (96.3)	0 (0.0)2 (100.0)	1 (2.0)50 (98.0)	0 (0.0)34 (100.0)	0.668^
Corneal arcus (<45 years) # Yes No	15 (13.8)94 (86.2)	0 (0.0)2 (100.0)	42 (82.4)9 (17.6)	0 (0.0)34 (100.0)	<0.001^
Lipid Profiles
TC (mmol/L)	5.2 (4.5–6.6) a	4.0 (3.5–4.8) a, b, c	6.9 (5.9–7.6) a, b, d	5.5 (4.8–6.2) c, d	<0.001*
TG (mmol/L)	1.6 (1.2–2.1) a	1.3 (1.0–1.9) a, b, c	1.8 (1.5–2.5) a, b	1.9 (1.5–2.7) a, c	<0.001*
LDL-C (mmol/L)	3.3 (2.6–4.7) a	2.0 (1.6–2.8) a, b, c	5.0 (3.8–5.3) a, b, d	3.4 (2.7–4.0) c, d	<0.001*
Pre-treatment LDL-C (mmol/L) **, 1	5.1 (4.5–5.9) a, b	2.4 (1.7–3.3) a, c, d	5.1 (4.0–5.7) c, e	3.5 (2.6–4.0) b, d, e	<0.001*
HDL (mmol/L)	1.1 (0.9–1.3) a	1.2 (1.0–1.4)	1.2 (1.0–1.5) a, b	1.1 (0.9–1.3) b	0.042*

Data presented as number (n) and percentage (%) for categorical data, mean and standard deviation (SD) and median (interquartile range) [IQR] for continuous data.

^Chi-squared test.

@p<0.05, One-Way ANOVA test.

*p<0.05, Kruskal Wallis test.

**Representing baseline LDL-C level prior to lipid-lowering medication, and LDL-C level for drug-naïve individuals.

#Patients without available data were excluded from the analysis.

1Several patients do not have the exact baseline lipid profile–statin conversion was used to calculate the estimated post-treatment LDL-C level.

a, b, c, d, e = p<0.05. Statistical tests with same symbols in a same row are significantly different with each other.

FH: Familial hypercholesterolaemia; PCAD: Premature coronary artery disease; HC: Hypercholesterolaemia; BMI: Body-mass index; LDL-C: Low-density lipoprotein cholesterol; TC: Total cholesterol; TG: Triglyceride; HDL: High-density lipoprotein.

†No data available.

Table 2

Factors associated with PCAD among all individuals in the study (n = 572).

Variables		Crude ORa(95% CI)	Adjusted ORb(95% CI)	Wald statisticsb (df)	p valueb
Age		1.002(0.982,1.021)	-	-	-
Gender (Male)		0.740(0.449, 1.221)	-	-	-
Ethnicity	Malay	0.633 (0.421, 0.953)
	Chinese	2.281 (1.231, 4.226)	-	-	-
	Indian & others	1.097 (0.664, 1.810)	-	-	-
Smoking	Current smoker	0.701 (0.476, 1.033)	-	-	-
	Ex-smoker	0.876 (0.598, 1.285)	-	-	-
	Non-smoker	1.438 (1.029, 2.010)	-	-	-
Familial Hypercholesterolaemia*		1.333 (0.951, 1.870)	107.034 (16.386, 699.146)	23.819 (1)	<0.001
Potential FH		3.727 (1.940, 7.158)	23.164 (3.996, 134.269)	12.286 (1)	<0.001
Definite FH		5.151 (1.507, 17.611)	-	-	-
Probable FH		2.949 (1.380, 6.300)	0.093 (0.014, 0.622)	6.001 (1)	0.014
Possible FH		0.825 (0.577, 1.180)	-	-	-
Unlikely FH		0.750 (0.535, 1.052)	-	-	-
Hypercholesterolaemia		169.991(71.956, 401.594)	-	-	-
Hypertension		8.922(6.086, 13.081)	3.262 (1.366, 7.786)	7.092 (1)	0.008
Diabetes mellitus		6.776(4.420, 10.388)	3.928 (1.421, 10.859)	6.957 (1)	0.008
BMI Categories	Normal	0.383 (0.222, 0.662)	-	-	-
	Overweight	0.798 (0.488, 1.305)	-	-	-
	Obese	2.133 (1.435, 3.170)	-	-	-
Family history of PCAD		1.660(1.041, 2.647)	-	-	-
Family history of HC		1.576(0.944, 2.632)	3.093 (1.151, 8.310)	5.014 (1)	0.025
High TCc		0.176 (0.122, 0.254)	-	-	-
High TGc		0.430 (0.306, 0.604)	-	-	-
Low HDL-Cc		0.994 (0.679, 1.455)	-	-	-
High Pre-treatment LDL-Cc		1.067 (0.724, 1.570)	-	-	-
High Post-treatment LDL-Cc		0.447 (0.269, 0.740)	-	-	-
On lipid lowering therapy		3.556 (2.328, 5.432)	-	-	-

aSimple logistic regression

bMultiple logistic regression.

*Familial Hypercholesterolaemia (FH) was defined as individuals that were clinically diagnosed as FH using DLCC criteria. Definite, Probable and Possible were recognised as FH and Unlikely FH were not FH.

OR: Odds ratio; CI: Confidence interval; PCAD: Premature coronary artery disease; BMI: Body mass index; HC: Hypercholesterolaemia; TC: Total cholesterol; TG: Triglyceride; HDL-C: High-density lipoprotein cholesterol; LDL-c: Low-density lipoprotein cholesterol.

cHigh TC: >5.2 mmol/L; high TG: >1.7 mmol/L; low HDL-C: <1.0 mmol/L (males), <1.2 mmol/L (females): High LDL-C: >4.9 mmol/L.

Table 3

Factors associated with PCAD among individuals with clinically diagnosed FH [DLCC score > 3]; (n = 246).

Variables		Crude ORa(95% CI)	Adjusted ORb(95% CI)	Wald statisticsb (df)	p valueb
Age		0.982 (0.954, 1.011)	-	-	-
Gender (Male)		0.788 (0.336, 1.849)	-	-	-
Ethnicity	Malay	0.190 (0.077, 0.470)	-	-	-
	Chinese	-	-	-	-
	Indian & others	2.796 (1.093, 7.152)	-	-	-
Smoking	Current smoker	1.273 (0.667,2.428)	-	-	-
	Ex-smoker	1.414 (0.763, 2.620)	-	-	-
	Non-smoker	0.742 (0.437, 1.259)	-	-	-
FamilialHypercholesterolaemia*		97.720 (28.814, 331.415)	-	-	-
Potential FH		3.753 (1.873, 7.517)	-	-	-
Definite FH		4.624 (1.329, 16.092)	9.736 (1.228, 77.214)	4.640 (1)	0.031
Probable FH		2.706 (1.227, 5.968)	-	-	-
Possible FH		0.266 (0.133, 0.534)	-	-	-
Hypertension		4.504 (2.572, 7.888)	3.310 (1.278, 8.570)	6.081 (1)	0.014
Diabetes mellitus		8.593 (3.881, 19.025)	4.644 (1.547, 13.936)	7.499 (1)	0.006
BMI Categories	Normal	0.506 (0.236, 1.083)	-	-	-
	Overweight	0.909 (0.398, 2.076)	-	-	-
	Obese	1.871 (1.035, 3.382)	-	-	-
Central obesity		2.368 (1.293,4.336)	-	-	-
Family history of PCAD		3.361 (1.736, 6.509)	3.303 (1.007, 10.832)	3.888 (1)	0.049
Family history of HC		2.844 (1.463, 5.529)	-	-	-
High TCc		0.170 (0.087, 0.332)	-	-	-
High TGc		0.749 (0.445, 1.260)	-	-	-
Low HDL-Cc		1.809 (0.957, 3.420)	-	-	-
High Pre-treatment LDL-Cc		0.751 (0.443, 1.273)	-	-	-
High Post-treatment LDL-Cc		0.285 (0.160, 0.507)	-	-	-
On lipid lowering therapy		4.841 (2.733, 8.573)	-	-	-

aSimple logistic regression

bMultiple logistic regression.

*Familial Hypercholesterolaemia (FH) was defined as individuals that were clinically diagnosed as FH using DLCC criteria. Definite, Probable and Possible were recognised as FH and Unlikely FH were not FH.

OR: Odds ratio; CI: Confidence interval; PCAD: Premature coronary artery disease; BMI: Body mass index; HC: Hypercholesterolaemia; TC: Total cholesterol; TG: Triglyceride; HDL-C: High-density lipoprotein cholesterol; LDL-c: Low-density lipoprotein cholesterol.

cHigh TC: >5.2 mmol/L; high TG: >1.7 mmol/L; low HDL-C: <1.0 mmol/L (males), <1.2 mmol/L (females): hHigh LDL-C: >4.9 mmol/L.

Table 4

Factors associated with PCAD among individuals with Potential FH [DLCC score > 6]; (n = 63).

Variables		Crude ORa(95% CI)	Adjusted ORb(95% CI)	Wald statisticsb (df)	p valueb
Age		0.896 (0.824, 0.974)	-	-	-
Gender (Male)		1.257 (0.226, 6.985)	-	-	-
Smoking	Current smoker	3.763 (0.442, 32.042)	-	-	-
	Ex-smoker	0.700 (0.193, 2.535)	-	-	-
	Non-smoker	0.700 (0.198, 2.472)	-	-	-
Definite FH		1.781 (0.429, 7.403)	-	-	-
Probable FH		0.561 (0.135, 2.333)	-	-	-
Hypertension		3.955 (0.957, 16.349)	-	-	-
Diabetes Mellitus		6.531 (0.781, 54.651)	-	-	-
BMI	Overweight	0.449 (0.037, 5.404)	-	-	-
	Obese	8.800 (2.175, 35.599)	165.704 (1.360, 20192.347)	4.349 (1)	0.037
Central obesity		5.911 (1.520, 22.992)	-	-	-
Family history of PCAD		3.368 (0.893, 12.707)	14.212 (1.226, 164.704)	4.508 (1)	0.034
Family history of HC		2.276 (0.550, 9.409)	-	-	-
High TGc		2.080 (0.556, 7.785)	-	-	-
Low HDL-Cc		2.727 (0.538, 13.825)	-	-	-
High Pre-treatment LDL-Cc		3.200 (0.472, 21.708)	-	-	-
High Post-treatment LDL-Cc		0.821 (0.233, 2.893)	-	-	-
On lipid lowering therapy		1.318 (0.374, 4.647)	-	-	-

aSimple logistic regression

bMultiple logistic regression.

OR: Odds ratio; CI: Confidence interval; FH: Familial Hypercholesterolaemia; PCAD: Premature coronary artery disease; BMI: Body mass index; HC: Hypercholesterolaemia; TC: Total cholesterol; TG: Triglyceride; HDL-C: High-density lipoprotein cholesterol; LDL-c: Low-density lipoprotein cholesterol.

cHigh TC: >5.2 mmol/L; high TG: >1.7 mmol/L; low HDL-C: <1.0 mmol/L (males), <1.2 mmol/L (females): High LDL-C: >4.9 mmol/L.

Table 5

Final model of independent predictors of premature coronary artery disease.

(A) Independent predictors of PCAD among all individuals (n = 572)
	B(SE)	Walda (df)	OR (95%CI)	p-value *
Diabetes	1.557 (0.249)	39.111 (1)	4.7 (2.91, 7.73)	<0.001
Hypertension	2.649 (0.303)	76.383 (1)	14.1 (7.81, 25.61)	<0.001
FH	1.054 (0.328)	10.326 (1)	2.9 (1.51, 5.46)	0.001
Potential FH	1.510 (0.385)	15.406 (1)	4.5 (2.13, 9.63)	<0.001
Hosmer and Lemeshow goodness-of-fit test, p-value: 0.914; Percentage prediction: 78.1%
(B) Independent predictors of PCAD among individuals with clinically diagnosed FH [DLCC score > 3] (n = 246)
	B(SE)	Walda (df)	OR (95%CI)	p-value *
Diabetes	1.852 (0.431)	18.465 (1)	6.4 (2.74, 14.83)	<0.001
Hypertension	1.293 (0.325)	15.806 (1)	3.6 (1.93, 6.89)	<0.001
Family history of PCAD	1.098 (0.381)	8.299 (1)	3.0 (1.42, 6.32)	0.004
Definite FH	1.964 (0.687)	8.178 (1)	7.1 (1.86, 27.40)	0.004
Hosmer and Lemeshow goodness-of-fit test, p-value: 0.656; Percentage prediction: 72.9%
(C) Independent predictors of PCAD among individuals with Potential FH [DLCC score > 6] (n = 63)
	B(SE)	Walda (df)	OR (95%CI)	p-value *
Family history of PCAD	1.901 (0.880)	4.668 (1)	6.7 (1.19, 37.53)	0.031
Obesity	2.732 (0.880)	9.639 (1)	15.4 (2.74, 86.21)	0.002
Hosmer and Lemeshow goodness-of-fit test, p-value: 0.150; Percentage prediction: 88.9%

*Statistically significant at ɑ = 0.05. Model is fit, model assumptions are met, no interaction and multicollinearity problems.

a Statistical test: Multiple Logistic Regression.