中文摘要：
APOA1/C3/A4/A5 基因簇在脂肪代謝過程中扮演重要角色，因此此基因簇之變異與血脂異常相關。腹部
肥胖經常伴隨血脂異常，且被認為是引起胰島素抗性之主要原因。此外血脂異常、肥胖與胰島素抗性
皆為增加女性罹患乳癌風險之因子。有鑑於此，肥胖患者經常被建議以減重來降低罹患代謝性疾病與
癌症之風險。在此研究中，我們系統性挑選五個 APOA1/C3/A4/A5 基因簇變異，並檢視這些變異是否
與肥胖者減重結果或乳癌患者術後存活向關。在肥胖減重部份，共有 398 位受試者(含 130 位接受減重
治療之病友)。我們發現位於 APOA5 之 rs662799 基因變異肥胖者之腰臀比過高與血液三酸甘油脂過高
呈現正相關；體重正常者中，rs662799 與代謝性指數無顯著關聯。尤其在男性肥胖者中，攜帶之 rs662799
數量與腰臀比和血液三酸甘油脂呈現性先關，且帶有 APOA5 rs662799 之男性病友在進行減重六個月後
腰臀比顯著降低。相對在與女性肥胖者 APOA5 rs662799 與腰臀比及減重效果無顯著關聯。因此在本研
究中，我們證實了 APOA1/C3/A4/A5 基因變異與腹部肥胖，且提供可預測減重結果之生物標記。而在
乳癌預後部份，共有 223 位乳癌確診且接受手術治療之病友與 162 位健康自願受試者接受追蹤及分析。
目前乳癌病人群的資料正在分析中。
Abstract
INTRODUCTION: The APOA1/C3/A4/A5 gene cluster is critically involved in lipid metabolism, and its
genetic polymorphisms are associated with dyslipidemia. Dyslipidemia often accompanies central obesity,
which is considered the pathogenic factor for insulin resistance. Furthermore, dyslipidemia, obesity and
insulin resistance are considered to increase breast cancer risk. As a result, weight-loss is often advised to
obese subjects for improving their metabolic index and reducing cancer risks. In this study, we aim to test the
efficacy of selected APOA1/C3/A4/A5 genetic polymorphisms in predicting successful weight-loss and breast
cancer survival.
METHOD: We selected seven single nucleotide polymorphisms (SNP) on the APOA1/C3/A4/A5 cluster, and
tested their predictive effects longitudinally in a case-control manner in two groups of participants. Participant
group 1 included 398 Taiwanese subjects (obese n=262; non-obese healthy n=136) from the Weight-loss
Clinic in National Cheng-Kung University Hospital (NCKUH), and 130 obese patients underwent weight-loss
treatments. On the other hand, female Taiwanese breast cancer patients (n=223) and healthy controls (n=162)
were recruited to participant group 2 in collaboration with the Department of Surgery, NCKUH.
RESULTS: In the participant group 1, APOA5 rs662799 minor allele (C) carriage was associated with
hypertriglyceridemia in obese patients of both genders, but increased waist-hip-ratio (WHR) in obese males
prior to treatment. In contrast, APOA5 rs662799 was not associated with metabolic parameters in non-obese
healthy controls. Furthermore, APOA5 rs662799 C allele-carrying male obese patients had significant WHR
reduction 6 months after weight-loss treatments, while this was not observed in obese females.
CONCLUSION: This study highlights the weight- and gender-specific effects of APOA5 rs662799 on central
obesity, and that weight-loss can ameliorate these effects. On the other hand, we are confident in finding the
carriage of tested SNPs correlated with breast cancer.
Introduction:
The APOA1/C3/A5 gene cluster transcribes for apolipoproteins (apo) A1, C3 and A5, which regulate
HDL-formation and lipoprotein lipase activity [1]. Several genetic polymorphisms situated in the
APOA1/C3/A4/A5 gene cluster are associated with dyslipidemia, insulin resistance and metabolic syndrome.
APOA1/C3/A4/A5 transcribes for apolipoproteins (apos) A1, C3, A4 and A5 [2], which participate in plasma
triglyceride (TG) and cholesterol metabolism. ApoA1 is the structural protein of high-density lipoprotein
(HDL), which is involved in reverse cholesterol transport [3]. Genetic polymorphisms in APOA1 are
associated with fasting and postprandial plasma lipids, and responses to medication for dyslipidemia [4, 5].
The promoter region-situated rs670 on APOA1 is correlated with HDL-cholesterol (HDL-C) levels, MetS, and
type 2 diabetes [6-8]. ApoC3 inhibits plasma lipoprotein lipase activity, and is found on triglyceride-rich
lipoproteins and HDL [9]. The single nucleotide polymorphisms (SNPs) within APOC3 coding or promoter
regions are associated with the altered TG metabolism [10, 11]. Two SNPs situated in the insulin-responsive
element of APOC3, rs2854116 and rs2854117 are known to be associated with postprandial plasma TG and
fatty liver [10, 12]. The function of apoA4 is less-known, but in vitro studies suggest its participation in
lipoprotein lipase activity and lecithin-cholesterol acyltransferase activity [13, 14]. APOA4 rs675, a missense
SNP that alters threonine347 to serine, is associated with response to fenofibrate treatment [15]. ApoA5
regulates TG metabolism and is proposed to interact with lipoprotein lipase activity [16, 17]. Thus mutations
and SNPs on APOA5, especially rs662799, rs3135506 and the Chinese-predominant rs2075291 are associated
with dyslipidemia and MetS [2, 18, 19].
Obesity has reached a globally epidemic level, and is forecasted to affect 800 million individuals by 2015
[20, 21]. Prolonged obesity increases risks of developing metabolic syndrome (MetS), type 2 diabetes and
cardiovascular diseases [21]. In particular, central obesity is considered more pathogenic than peripheral
obesity, and often occurs in parallel with dyslipidemia and insulin resistance [22, 23]. Central obesity can be
defined by elevated waist-hip-ratio (WHR) or waist circumference (WC), which has been designated to be the
prerequisite factor for the MetS criteria by the International Diabetes Federation (IDF)[24]. Therefore, active
weight-loss through weight loss surgery or planned diet regime in centrally-obese patients can improve their
glycemic and lipidemic controls [25, 26]. Interestingly, alterations in lipidemic control and ectopic fat
deposition have been observed concurrently in patients during viral infections and associated treatments [27,
28]. Moreover, mice models deficient in controlling lipid or glucose metabolism have developed
lipodystrophic or obesogenic phenotypes [29, 30].
On the other hand, breast cancer is currently the leading cause of cancer deaths in females worldwide,
and also the second most common cancer after lung cancer [31]. The current therapeutic regimens for
operable early-stage breast cancers include endocrine therapy, chemotherapy and radiotherapy. The
assessment tools for post-surgery planning are currently based on clinicopathological evaluations, but are
unsatisfactory requiring finer adjustments [32]. The risk factors for breast cancer poor prognosis include
positive sentinel lymph node metastasis, hormone receptor negativity, larger tumor size, younger age, and
menopausal status [32]. In contrast to the Western population, the breast cancer incidence peaks at a younger
age in Oriental Asians, which include Taiwanese [33, 34]. Nevertheless, the westernized dietary pattern and
lifestyle in Taiwan in the last two decades has increased the incidences of metabolic disorders, including
dyslipidemia, as well as breast cancer in Taiwanese females [34].
Both cohort and case-control epidemiological observations have demonstrated the association of
metabolic syndrome, obesity and diabetes with increased breast cancer risk [35, 36]. Dyslipidemia often occur
in parallel to obesity and diabetes, and is a component of metabolic syndrome [24]. Dyslipidemia in the forms
of hypertriglyceridemia, hypercholesterolemia, and low high density lipoprotein-cholesterol (HDL-C) have
been observed in breast cancer patients of several ethnic groups [37-40]. Moreover, in large cohort studies
dyslipidemia is associated with increased breast cancer risk and poor prognosis [41-43]. The intake of
lipid-lowering drugs or agents in women was associated with reduced breast cancer occurrence and recurrence
risk [44-46]. Consistently, mammary tumor growth and metastasis was accelerated in a hyperlipidemic murine
model [47].
Though the effect of APOA1/C3/A4/A5 gene cluster polymorphisms on dyslipidemia has been studied in
great detail, the contributions these polymorphisms in central obesity are less known, especially in Asian
populations. Furthermore, the effect of weight-loss in obese patients carrying APOA1/C3/A4/A5 SNPs in
improving associated metabolic factors is not yet established. Similarly, the contributions of
APOA1/C3/A4/A5 SNPs to breast cancer has not been determined in detail. In this study, we first tested the
correlation of 5 selected well-known SNPs on the APOA1/C3/A5 gene cluster with metabolic parameters and
breast cancer in a case-control manner. Furthermore, we analyzed the effect of the SNPs associated with
obesity-associated anthropometric and metabolic parameters in the obese patients 6 months after they have
initiated weight-loss intervention, and with breast cancer post-surgery outcome after a mean follow-up period
of 10.4 years. Finally, we specifically examined the prognostic value of APOA1 rs670 in lymph node-negative
breast cancer patients.
Materials and methods
Patient recruitment
Participants group 1 for study on metabolic parameters
Taiwanese non-obese healthy (BMI<25) volunteers and obese Taiwanese patients (BMI≥25) visiting the
Weight Management Clinic and at National Cheng Kung University Hospital (NCKUH), Tainan, Taiwan from
2007 to 2010 were recruited to participate in this study. Exclusion criteria included current smokers, high
alcohol consumers (more than two drinks daily), diagnosis of inflammatory disease or cancer, and use of
hormone replacement therapy. In total 262 obese patients (112 males and 150 females) and 136 non-obese
participants (50 males and 86 females) were enrolled. Of the recruited obese patients, 55 males and 75
females underwent weight-loss intervention and returned for regular visits over a 6 month period (study
endpoint). The weight-loss intervention methods were balanced low calorie diet (500 Kcal deficits per day)
alone (18 males and 32 females) [48], diet with 120mg Orlistat (Roche) t.i.d (6 males and 4 females) [48], diet
with Sibutramine (Abbot) 10 mg or 15 mg daily (19 males and 25 females) [48] and bariatric surgery
(mini-gastric bypass/sleeve, 12 males and 14 females). Patient compliance of the diet program was supervised
by trained professional dieticians reviewing patient’s nutritional diary during regular clinic visits of every 3
weeks. The study received approval from the local institutional review board, and all patients gave informed
written consent.
Participants group 2 for study on breast cancer
Taiwanese female breast cancer patients (n=223, 48.4±10.2 years, ranged 29–75 years) received surgical
intervention plus axillary/sentinel lymph node dissection during 1999-2005 at National Cheng Kung
University Hospital (NCKUH) and Tainan Hospital, and were followed-up to November 2012. Healthy female
controls (n=162, 43.0 ± 8.8 years, ranged 19.0–69.0 years.) were also recruited. This is a continuation study of
Hsiao et al., 2004 [49]. Bodyweight (kg) and body height (m) were measured at the time of mammectomy,
and used for calculating body mass index (BMI, kg m-2). The diagnosis was confirmed by histological
examinations of mammary and node specimens. Estrogen receptor (ER) and progesteron receptor (PR)
expressions in primary breast tumor were determined as described previously [50]. Individuals with at least
one first-degree or second-degree female relatives affected by breast cancer were considered to have a family
history. This information was obtained by interview with patients and their family members. The recruited
patients received tamoxifen (TAM) (n=92), TAM and chemotherapy (n=56), TAM and radiotherapy (n=10),
and triple-therapy (n=55). This study received approval from the local institutional review board (NCKUH
IRB) and signed informed consent was obtained from the patients.
Anthropometric and laboratory measurements
At 0-month and after 6-month of weight-loss intervention, patients underwent physical examination by
trained personnel who took anthropometric measurements, including body height, bodyweight (BW), WC,
and hip circumference (HC). Body mass index (BMI) was calculated as BW (kg) ∕ [body height (m)]2. Whole
venous blood was collected after overnight fasting, and at 2hr post 75g oral glucose challenge. Waist-hip ratio
(WHR) was calculated as WC (cm) /HC (cm). Plasma levels of total TG, total cholesterol (TC), HDL-C,
low-density lipoprotein cholesterol (LDL-C), and total glucose were measured with an automated instrument
biochemically (Roche Modulator DP, Roche, St. Louis, MO, USA). Fasting serum insulin was measured by
radioimmunoassay (Coat-A-Count Insulin In-vitro Diagnostic Test Kit, Siemens, Los Angeles, CA, USA).
Homeostasis model assessment for insulin resistance (HOMA-IR) was calculated as [fasting plasma glucose (FPG) (mmol/L) × fasting serum insulin (U/mL)] ∕22.5. The MetS definition criteria used in this study
comply with that proposed by the IDF with appropriate WC adjustments for Asians [51]. In short, MetS is
diagnosed in the presence of elevated WC (WC≥80 cm in females and ≥90 cm in males) plus two of the
following: (i) hypertriglycedemia (hyper-TG; TG ≥1.7 mmol∕L); (ii) low HDL-C (HDL-C <1.3 mmol ∕L in females and <1.0 mmol ∕L in males); (iii) impaired fasting glucose (IFG) (fasting glucose ≥5.6 mmol∕L); and
(iv) hypertension (systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥90 mmHg).
Impairment in glucose tolerance (IGT) was defined plasma glucose 2hr after oral glucose challenge (PC-2hr)
≥7.8 mmol/L without concurrent IFG. The cut-off values used for elevated WHR were ≥1.0 in males and ≥0.9
in females.
Genomic DNA extraction, SNP genotyping and linkage analysis
Genomic DNA was extracted from white blood cells using the Puregene DNA Isolation Kit (Gentra
Systems, Minneapolis, MN, USA). According to criteria described in Hsu et al. [52], we selected five SNPs
on the APOA1/C3/A4/A5 gene cluster that follows: APOA1 rs670, APOC3 rs2854116, APOC3 rs2854117,
APOA5 rs662799 and APOA5 rs2075291. The SNP genotypes were determined using commercial real-time
PCR primer and probes from Applied Biosystems (ABI, Foster City, CA, USA) (APOA5 rs662799 and
APOA5 rs2075291) and TIB MOBIOL (Berlin, Germany) (APOA1 rs670, APOC3 rs2854116 and APOC3
rs2854117). Fluorescence data from real-time PCRs were collected by a Step-One-Plus Sequence Detection
System (ABI) or LightCycler 480 (Roche, St. Louis, MO, USA). Haploview [53] was used for the analysis of
SNP linkage disequilibrium, and Hardy-Weinberg equilibrium, and haplotype analysis. SNP linkage
disequilibrium test results with logarithm of odds (LOD) socores ≥2 and pair-wise D’>0.80 were considered
as significant linkage.
Statistical analysis
The association of SNP with breast cancer risk, baseline clinical parameters and post-surgery progression
was analyzed by Chi-squared test. The odds ratio for unfavorable baseline characteristics and events in
post-surgery progression was analyzed by binary logistic analysis. The differences in BMI, age, and mean
years in survival were analyzed by one-way ANOVA. The survival curves of APOA1 rs670 genotype carriers
were plotted by Kaplan-Meier analysis. The hazard ratio for overall and recurrence-free survival was
calculated by Cox proportional-hazards regression analysis. Possible confounders including unfavorable
baseline characteristics, age and BMI were adjusted for in regression analysis. Statistical analysis was
performed using SPSS 13 (SPSS Inc., Chicago, DE, USA). In all cases, p-values ≤0.05 were considered
statistically significant.
Results 1
Demographic data of enrolled obese and non-obese patients
Two hundred and sixty-two obese (BMI≥25) patients and 136 non-obese (BMI<25) healthy volunteers
were recruited from the Weight Management Clinic and routine health check-up in NCKUH from 2007 to
2010. Table 1 summarizes the characteristics of the recruited patients, indicating that the obese patients had
significant higher anthropometric parameters as BW, BMI, body fat, WC, HC and WHR, and less favorable
metabolic parameters as higher TG, TC, LDL-C, plasma glucose, fasting insulin, HOMA-IR, and lower
HDL-C than non-obese healthy volunteers (Table 1A, all p values <0.001). Analyzed based on the IDF
guidelines, our results indicated that recruited obese patients had higher proportions of central obesity (as
elevated WC or elevated WHR), dyslipidemia, impaired glycemic control, hypertension and MetS than
non-obese individuals (Table 1B, all p values <0.001). The gender distribution of obese and non-obese
participants were comparable (Table 1B, p=0.282). Of the 7 genotyped SNPs, APOA4 rs675 and APOA5
rs3135506 were homozygotes of the major allele in all recruited patients. The genotype distributions of
APOA1 rs670, APOC3 rs2854116, APOC3 rs2854117, APOA5 rs662799, and APOA5 rs2075291 were
comparable in obese and non-obese individuals (Data not shown). The five SNPs were analyzed against
anthropometric and metabolic parameters. APOA1 rs670, APOC3 rs2854116, APOC3 rs2854117 and APOA5
rs2075291 showed no or minimal association with tested parameters in obese or non-obese participants. In
contrast, APOA5 rs662799 showed significant associations with metabolic profiles in obese patients (Table 2).
APOA5 rs662799 correlated with unfavorable metabolic profiles in obese patients
APOA5 rs662799 showed significant associations with TG and TC in obese patients (Table 2): The mean
values for TG and TC were significantly different among APOA5 rs662799 genotype carriers (p<0.001 and
p=0.044, respectively), but only the association with TG showed linearity (p<0.001). Moreover, significantly
higher proportions of obese APOA5 rs662799 C allele carriers (T/C+C/C) were centrally obese than non-C
allele carriers (T/T) in the sense of elevated WHR (32.38% vs. 19.67%, p=0.033) (Table 3A). The C allele
carriers were more likely to have hyper-TG, low HDL-C, and MetS than non-C carriers (hyper-TG: 37.14% vs.
16.39%, p<0.001; low HDL-C: 62.86% vs. 45.08%, p=0.011; and MetS: 52.38% vs. 35.25%, p=0.015).
Though APOA5 rs662799 was associated with hypertriglyceridemia in non-obese participants, the number of
patients with the metabolic condition was minimal (Table 3B).
Gender-dependent effect on associations of APOA5 rs662799 minor allele carriage with central obesity
and MetS in obese patients.
Upon further analysis, we found that gender contributed to APOA5 rs662799’s association with
metabolic parameters in obese patients (Figure 1). Gender-genotype interactions were observed in WHR
(p=0.004, Figure 1A) and TG (p<0.001, Figure 1B) but not BMI (Figure 1C) and HDL-C (Figure 1D) of
obese patients. The mean values of WHR (p<0.001, Figure 1A) and TG (p<0.001, Figure 1B) were
significantly different among male APOA5 rs662799 genotype carriers.
One APOA5 rs662799 C allele conferred to increase of 0.032 unit of WHR (95%CI=0.014-0.050,
p=0.001) and 0.905 mmol/L TG (95%CI=0.445-1.365, p<0.001) in obese males after appropriate adjustments
(Table 4A). The correlations of APOA5 rs662799 C allele with WHR and TG were independent, as the
statistical significance sustained after adjustments for TG and WHR, respectively (Table 4A). Obese male C
allele carriers had increased odds of elevated WHR by 6.52-fold (95%CI=1.87-22.73, p=0.003) as compared
with non-C allele carriers after adjustments for age, BMI and TG (Table 4B). Though the frequency of MetS
increased in obese males APOA5 rs662799 C allele carriers as compared with non-C allele carriers, the effect
was diminished after additional adjustment for WHR (Table 4B).
The unfavorable metabolic profiles contributed by APOA5 rs662799 in obese males at baseline were no
longer present after significant weight-loss.
One-hundred-and-thirty of the 262 obese patients initiated weight-loss intervention, and completed the
return visit 6 months later, and their clinical data were analyzed longitudinally. The baseline BMI of APOA5
rs662799 C allele carriers and non-C allele carriers were comparable in obese male (p=0.530, Figure 2A).
Moreover, 6 months after weight-loss intervention, male APOA5 rs662799 C and non-C allele carriers
achieved significant improvement in BMI (p<0.001 and p=0.015 in males, Figure 2A). The number of patients
treated with diet alone, diet with Orlistat (Roche), diet with Sibutramine (Abbot) or bariatric surgery were
comparable (p=0.632) as shown in Figure 2A.
Interestingly, significant improvements in WHR was achieved in APOA5 rs662799 C allele carriers
(0.98±0.05 to 0.94±0.05, p=0.001, Figure 2B). On the contrary, the changes in WHR were not evident in
APOA5 rs662799 non-C allele males (0.93±0.05 to 0.92±0.07, p=0.267). Of note, significant improvements of
male APOA5 rs662799 C allele and non-C allele carriers were observed in both WC (p<0.001 and p=0.004)
and HC (p<0.001 and p=0.002). The significant improvement in WHR at 6 months after weight-loss
intervention resulted in the comparable WHR (p=0.240, Figure 2B) between male APOA5 rs662799 C allele
and non-C allele carriers at study end-point.
Results 2
Demographic characteristics of patient group 2
The baseline characteristics of the recruited breast cancer patients are shown in Table 5. The mean BMI
of breast cancer patients at baseline was 23.36±3.79 kg m-2. The major tumor type of recruited patients was
infiltrating ductal carcinoma (86.55%), and the tumor occurrence side was evenly distributed (right breast:
44.84%, left breast: 51.12%, bilateral: 4.04%). The recruited patients were mainly of early-stage breast cancer,
as 71.30% of patients had tumors <5cm, 69.06% were of tumor stages 0-2, and 64.57% of patients had single
or double positive for ER and PR. Additionally, 53.81% of patients had detectable lymph node involvement,
and only 5.83% had a family history of breast cancer. We are currently reviewing the patient charts, and
should have conclusive findings in the next few months.
Conclusions
In conclusion, this study conducted a systematic and through longitudinal analysis on the effects of the
well-known SNPs on APOA1/C3/A4/A5 in gender-stratified obese patients undergoing weight-loss
intervention. We showed that APOA5 rs662799 polymorphism (C allele carriers: T/C and C/C) was
unfavorable in obese patients; however, this predicament was improved prominently by weight management.
Despite our relatively small sample size, appropriate statistical adjustments demonstrated the consistency of
our main findings. Moreover, baseline observations and preliminary analysis in normal weight volunteers
supported the intervention outcome of the patients, despite the varied intervention methods among the patients.
This pioneering study supports the generally accepted view of weight maintenance in maintaining a healthy
metabolic profile. Results of this study provide further insight into the benefits of weight-loss intervention in
Asian obese patients with central obesity and dyslipidemia. The findings have been published on Nutrition
and Diabetes, 2013.
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Table 1. Baseline characteristics of obese and non-obese participants.
(A)
Obese; BMI≥25 (N=262) Non-obese; BMI<25 (N=136)
Mean±S.D. Min-Max Mean±S.D. Min-Max
Age (years) 35±10.45 18-72 31±8.51 18-72
BW (kg) 93.0±22.77 57.6-188.5 55.8±8.05 41.0 -77.9
Body fat (%) 42.0±9.40 19.9-73.3 23.6±6.52 11.9-73.2
BMI (kg/m2) 33.7±6.20 25.0-60.0 20.6±2.03 16.0 -24.9
Circumference
WC (cm) 101±14.71 72-158 71±7.09 54-90
HC (cm) 113±11.99 90-157 91±5.15 69-103
WHR 0.89±0.08 0.73-1.13 0.78±0.07 0.64-0.93
Blood pressure (mmHg)
SBP 119±17.43 78-173 103±11.17 76-133
DBP 74±11.18 48-108 68±8.94 47-107
Plasma lipids
TG (mmol/L) 1.53±1.12 0.42-9.27 0.90±0.46 0.4-3.7
Cholesterol (mmol/L)
TC 4.94±1.02 2.49-10.31 4.58±0.84 2.98-6.81
HDL-C 1.17±0.28 0.57-2.43 1.60±0.33 0.96-2.56
LDL-C 3.07±0.89 0.65-6.63 2.57±0.77 1.09-4.92
Plasma glucose (mmol/L)
FPG 5.45±1.40 0.54-14.36 4.96±0.52 3.85-7.32
PC-2hr 7.34±2.81 2.7-20.85 5.70±1.49 3.3-12.6
Fasting plasma insulin (U/mL) 16.08±18.37 1.55-182.45 4.63±3.31 0.12-14.87
HOMA-IR (unit) 4.28±7.06 0.31-91.45 1.04±0.80 0.03-3.51
(B)
Obese; BMI≥25 (N=262) Non-obese; BMI<25 (N=136)
Number of patients (%) Mean±S.D.
Gender (M/F) 112 (42.7%) /150 (57.3%) 50 (36.8%) / 86 (63.2%)
Central obesity
Elevated WC 243 (92.75%) 5 (3.68%)
Elevated WHR 65 (24.81%) 1 (0.74%)
Hyper-TG 72 (27.48%) 5 (3.68%)
Low HDL-C 142 (54.20%) 9 (6.62%)
Impaired glycemic control
IFG 78 (29.77%) 12 (8.82%)
HOMA-IR >1.82 unit 173 (66.03%) 20 (14.71%)
IGT 97 (37.02%) 10 (7.35%)
Hypertension 67 (25.57%) 5 (3.68%)
MetS 116 (44.27%) 3 (2.21%)
Abbreviations: BW, bodyweight; BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist-hip-ratio; SBP, systolic blood pressure;
DBP, diastolic blood pressure; TG, plasma triglycerides; TC, total plasma cholesterol; FPG, fasting plasma glucose; PC-2hr, plasma glucose 2hr post oral challenge;
HOMA-IR, homeostasis model assessment for insulin resistance; hyper-TG, hypertriglyceridemia; MetS, metabolic syndrome; IGT, impaired glucose tolerance.
Cut-off values: elevated WC, WC≥80 cm in females and ≥90 cm in males; elevated WHR, WHR≥0.9 in females and ≥1.0 in males; hyper-TG, TG ≥1.7 mmol∕L;
low HDL-C, HDL-C <1.3 mmol ∕L in females and <1.0 mmol ∕L in males; IFG, fasting glucose ≥5.6 mmol∕L; IGT, PC-2hr ≥7.8 mmol/L .The mean±S.D. is shown for continuous parameters, and the number of patients (%) is shown for those with metabolic conditions.
Table 2. The metabolic parameters and the metabolic condition frequencies of APOA5 rs662799 genotype carrier in obese and non-obese participants.
Obese (BMI≥25) Non-obese (BMI<25)
T/T (N=122)
T/C (N=81)
C/C
(N=24) p valuea p valueb
T/T (N=64)
T/C (N=55)
C/C
(N=10) p valuea p valueb
Age (years) 34±10.25 34±10.48 35±9.75 0.918 0.685 32±9.43 30±7.09 31±7.25 0.635 0.507
Gender (M/F)
BW (kg) 93.2±23.19 95.2±24.68 92.8±19.39 0.822 0.830 56.1±7.35 55.0±7.90 57.6±12.24 0.563 0.937 Body fat (%) 42±9.80 42.3±9.76 43.5±7.4 0.782 0.547 24±8.18 22.5±4.35 24.9±4.65 0.338 0.631 BMI (kg/m2) 33.6±6.57 34.3±6.23 33.7±4.73 0.691 0.620 20.7±2.19 20.2±1.69 21.1±2.34 0.254 0.666 Circumference
WC (cm) 100.1±14.52 102.6±15.24 101.4±15.53 0.490 0.372 71.4±6.59 69.9±6.45 72±11.53 0.440 0.602 HC (cm) 112.7±12.76 113.4±12.13 110.7±9.18 0.634 0.709 91.2±4.85 90.1±5.07 92.1±5.56 0.357 0.714 WHR 0.89±0.07 0.90±0.08 0.91±0.100 0.145 0.052 0.78±0.06 0.78±0.07 0.78±0.10 0.873 0.690 Blood pressure (mmHg)
SBP 119±16.33 118±15.90 120±22.83 0.757 0.805 103±10.52 103±11.42 103±12.17 0.997 0.950
DBP 74±11.75 74±10.25 75±12.86 0.824 0.673 68±7.70 68±8.53 67±10.12 0.947 0.756
Plasma lipids
TG (mmol/L) 1.26±0.69 1.65±1.18 2.29±2.06 <0.001 <0.001 0.83±0.43 0.92±0.35 1.2±1.00 0.075 0.037 Cholesterol (mmol/L)
TC 4.97±1.00 4.79±0.88 5.37±1.38 0.044 0.474 4.54±0.86 4.65±0.77 4.39±1.01 0.612 0.922 HDL-C 1.21±0.27 1.12±0.25 1.19±0.39 0.057 0.158 1.62±0.31 1.61±0.31 1.47±0.47 0.443 0.387 LDL-C 3.18±0.86 2.94±0.80 3.13±1.20 0.164 0.266 2.54±0.83 2.61±0.65 2.37±0.87 0.658 0.904 Plasma glucose (mmol/L)
FPG 5.46±1.46 5.4±1.31 5.22±1.25 0.729 0.460 4.98±0.56 4.93±0.38 4.82±0.44 0.581 0.309 PC-2hr 7.12±2.84 7.76±2.65 6.86±2.25 0.190 0.626 5.73±1.44 5.64±1.64 5.8±1.18 0.924 0.899 Fasting plasma insulin
(U/L) 13.77±11.07 18.41±24.81 16.24±14.26 0.186 0.169 4.62±3.42 4.63±3.26 4.12±3.34 0.908 0.792
Abbreviations: BW, bodyweight; BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist-hip-ratio; SBP, systolic blood pressure;
DBP, diastolic blood pressure; TG, plasma triglycerides; TC, total plasma cholesterol; FPG, fasting plasma glucose; PC-2hr, plasma glucose 2hr post oral challenge;
HOMA-IR, homeostasis model assessment for insulin resistance; MetS, metabolic syndrome; IGT, impaired glucose tolerance. Values of mean±S.D. are shown for each parameters. a shows the p value of ANOVA; b shows the p value of linearity test in ANOVA.
Table 3. Association of anthropometric and metabolic conditions with APOA5 rs662799 genotypes in obese (BMI≥25) individuals and non-obese (BMI<25) individuals.
(A)
Genotype C allele dominant C allele recessive
Obese patients
(n=262) T/T T/C C/C p value non-C carrier
(T/T)
C carrier
(T/C+C/C) p value non-C/C
(T/T+T/C) C/C p value
Total 122 81 24 - 122 105 - 203 24 -
Elevated WHR 24 (19.67%) 24 (29.63%) 10 (41.67%) 0.045 24 (19.67%) 34 (32.38%) 0.033 48 (23.65%) 10 (41.67%) 0.080 Hyper-TG 20 (16.39%) 30 (37.04%) 9 (37.50%) 0.002 20 (16.39%) 39 (37.14%) <0.001 50 (24.63%) 9 (37.50%) 0.218 Low HDL-C 55 (45.08%) 52 (64.20%) 14 (58.33%) 0.029 55 (45.08%) 66 (62.86%) 0.011 107 (52.71%) 14 (58.33%) 0.670 MetS 43 (35.25%) 43 (53.09%) 12 (50.00%) 0.038 43 (35.25%) 55 (52.38%) 0.015 86 (42.36%) 12 (50.00%) 0.519
(B)
Genotype C allele dominant C allele recessive
Non-obese individuals
(n=136) T/T T/C C/C p value non-C
carrier (T/T)
C carrier
(T/C+C/C) p value non-C/C
(T/T+T/C) C/C p value
Total 64 55 10 - 64 65 - 119 10 -
Elevated WHR 0 (0.00%) 1 (1.23%) 0 (0.00%) 0.508 0 (0.00%) 1 (1.54%) 1.000 1 (0.84%) 0 (0.00%) 1.000 Hyper-TG 1 (0.82%) 1 (1.23%) 2 (8.33%) 0.003 1 (1.56%) 3 (4.62%) 0.619 2 (1.68%) 2 (20.00%) 0.025 Low HDL-C 3 (2.46%) 2 (2.47%) 2 (8.33%) 0.072 3 (4.69%) 4 (6.15%) 1.000 5 (4.20%) 2 (20.00%) 0.078 MetS 1 (0.82%) 1 (1.23%) 1 (4.17%) 0.200 1 (1.56%) 2 (3.08%) 1.000 2 (1.68%) 1 (10.00%) 0.199
Abbreviations: WC, waist circumference; WHR, waist-hip-ratio; hyper-TG, hypertriglyceridemia; IFG, impaired fasting glucose; HOMA-IR, homeostasis model assessment for insulin resistance; IGT, impaired glucose tolerance; MetS, metabolic syndrome. Cut-off values: elevated WC, WC≥80 cm in females and ≥90 cm in males; elevated WHR, WHR≥0.9 in females and ≥1.0 in males; hyper-TG, TG ≥1.7 mmol∕L; low HDL-C, HDL-C <1.3 mmol ∕L in females and <1.0 mmol ∕ L in males; IFG, fasting glucose ≥5.6 mmol∕L; IGT, PC-2hr ≥7.8 mmol/L .Chi-squared test is used for determining statistical significance.
Table 4. The number of APOA5 rs662799 C allele is linearly associated with WHR and TG in obese males, and contributes to increased odds of elevated WHR and hypertriglyceridemia.
(A)
Number of rs662799 C allele Unadjusted linear regression Adjusted linear regression
0 1 2 p
value# Co-efficient 95% CI p
value Co-efficient 95% CI p value Mean ± S.D
Obese males
WHR (unit) 0.92±0.05 0.97±0.06 1.00±0.07 <0.001 0.041 0.024-0.058 <0.001 0.032a 0.014-0.050a 0.001a TG (mmol/L) 1.42±0.83 1.9±1.48 3.93±2.61 <0.001 0.953 0.541-1.364 <0.001 0.905b 0.445-1.365b <0.001b (B)
Obese patients (BMI≥25)
rs662799 Unadjusted* Adjusted*
Non-C (T/T)
C carriers
(T/C+C/C) p value@ Odds
ratio 95% CI p value Odds
ratio 95% CI p value Males
Total 54 46 -
Elevated WHR 4 (7.41%) 17 (36.96%) <0.001 7.33 2.25-23.88 0.001 6.52a 1.87-22.73a 0.003a Hyper-TG 13 (24.07%) 21 (45.65%) 0.034 2.649 1.13-6.21 0.025 1.929b 0.728-5.11b 0.186b
Low HDL-C 22 (40.74%) 25 (54.35%) 0.228 - -
MetS 23 (42.59%) 30 (65.22%) 0.028 2.53 1.12-5.69 0.025 0.61b 0.18-2.02b 0.416b
Abbreviations: BMI, body mass index; hyper-TG, hypertriglyceridemia; MetS, metabolic syndrome; TG, plasma triglyceride; WHR, waist-hip-ratio. Cut-off values:
elevated WHR, WHR≥ ≥1.0 in males; hyper-TG, TG ≥1.7 mmol∕L; low HDL-C, HDL-C <1.0 mmol ∕L in males. p value# was analyzed by the Pearson’s correlation test. p value@ was analyzed by the chi-squared test. * were analyzed by binary logistic regression. a were adjusted by age, BMI and TG. b were adjusted by age, BMI and WHR.
Table 5. Demographic data of recruited breast cancer patients (n=223).
Parameters Values
Baseline
Age (years) 48.4±10.2 (29-75)
BMIa (kg m-2) 23.36±3.79 (15.06-38.05)
Tumor type
Infiltrating ductal carcinoma 193 (86.55%)
Ductal carcinoma in situ 17 (7.62%)
Others 11 (4.93%)
Unknown 2 (0.90%)
Side
Right 100 (44.84%)
Left 114 (51.12%)
Both 9 (4.04%)
Tumor size
<5cm 159 (71.30%)
≥5cm 26 (11.66%)
Unknown 38 (17.04%)
Lymph node involvement
Positive 121 (53.81%)
Negative 90 (40.36%)
Unknown 12 (5.83%)
TMN staging
Stages 0-2 154 (69.06%)
Stages 3-4 30 (13.45%)
Unknown 39 (17.49%)
ER/PR status
Single or double positive 144 (64.57%)
Negative 52 (23.32%)
Unknown 27 (12.11%)
Family history
Positive 13 (5.83%)
Negative 203 (91.03%)
Unknown 7 (3.14%)
Post-surgery follow-up Therapies
TAM only 92 (41.26%)
TAM and chemotherapy 56 (25.11%)
TAM and radiotherapy 10 (4.48%)
Triple therapy 55 (24.66%)
Unknown 10 (4.48%)
Recurrence
With 56 (25.11%)
Without 167 (74.89%)
Mortality
With 33 (14.80%)
Without 190 (85.20%)
Progression-free 165 (73.99%)
Mean survival years
Recurrence-free 9.26±5.31 (0.04-24.52)
Overall 10.14±5.11 (0.14-24.52)
NOTE: Values shown are mean±s.d., or number of patients (% of n).
aData from 185 patients. Bold type indicates p<0.050.
Figure 1
(A) (B)
(C) (D)
Figure 2
(A) (B) (C)
