Perilipin Gene Variation Determines Higher Susceptibility to Insulin Resistance in Asian Women When Consuming a High–Saturated Fat, Low-Carbohydrate Diet

Participants were recruited in the framework of the 1998 Singapore National Health Survey. The detailed methodology of this survey of a nationally representative sample has been described elsewhere (13). Briefly, the survey protocol was based on the World Health Organization–recommended model for field surveys of diabetes. A stratified and systematic sampling was used to select individuals aged 18–69 years from the National Database on Dwellings, with oversampling of the minority groups (Malays and Indians) to ensure that prevalence estimates were reliable. The ethnic composition of the sample was 64% Chinese, 21% Malays, and 15% Asian Indians and included 4,723 individuals. The study was approved by the Ministry of Health in Singapore, and informed consent was obtained from all participants. We included in our analyses 4,107 subjects (1,909 men and 2,198 women) who had DNA and PLIN genotypes successfully determined. Demographic, clinical, biochemical, genetic, and lifestyle data (alcohol and tobacco consumption and physical activity) were obtained in all these participants as previously reported (11,13). There were no differences in these variables between participants and subjects in which genetic analysis was not available.

A previously validated food frequency questionnaire (14) was administered to a systematic random sample of the 4,723 participants (1 in 2). This random sample did not differ in any characteristic from the initial sample. A list of 159 individual food items grouped into 23 main food types and 25 subfood types were included in this questionnaire. Each food group was carefully considered for ensuring the representatives of listed foods in the three ethnic groups. Nutrient contents were estimated using the food composition database of the Singapore Ministry of Health. Energy intake, carbohydrate, proteins, total fat, saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) were computed. To avoid the potential bias caused by the change in habitual dietary patterns in diabetic subjects who know their disease, diet was only analyzed in subjects who initially did not have diagnosis of diabetes. The gene-diet interaction analysis was carried out in 2,154 participants (1,001 men and 1,153 women).

Weight, height, total cholesterol, and triglycerides were determined as previously described (11,13). Fasting glucose (Boehringer Manheim, Mannheim, Germany) and fasting insulin (immunoassay using an Abbott AxSYM; Abbott Laboratories, Chicago, IL) were determined in all participants (13). After the fasting sample collection, a 75-g oral glucose tolerance test was taken for all subjects except diabetic subjects on medication (n = 3,884). A total of 75 g glucose dissolved in 250 ml of water was ingested. After 2 h, a second blood sample was obtained, and glucose (2-h glucose) and insulin (2-h insulin) were measured. Insulin resistance (fasting glucose × fasting insulin/22.5) was calculated using the homeostasis model assessment of insulin resistance (HOMA-IR) method (15). Initially, previously diagnosed diabetes was assessed by questionnaire. In addition, following biochemical determinations, subjects with fasting glucose ≥7.0 mmol/l or 2-h glucose ≥11.1 mmol/l were also classified as having diabetes (16).

DNA was isolated from blood, and, based on our previous results, two common PLIN polymorphisms (PLIN 11482G→A and PLIN 14995A→T) were determined by multiplex PCR on an ABI Prism 3100 genetic analyzer using Genotyper Version 3.7 (Applied Biosystems, Foster City, CA) as previously described (11).

A general inheritance model was fitted and a recessive model was finally used based on observed effects. Multiple linear regression models with interaction terms were used to analyze the associations and interactions between genotypes and insulin resistance. Estimated means were adjusted for age, BMI, ethnicity, smoking, alcohol consumption, and exercise. Additional adjustment for energy intake and other macronutrients (total fat or carbohydrate) was included in the dietary analysis as indicated. Dietary fat (total, SFAs, MUFAs, or PUFAs) and carbohydrate intake were first treated as categorical according to the population tertiles. In addition, the SFA-to-carbohydrate ratio was computed as continuous. Logarithm transformation was done for fasting glucose, fasting insulin, 2-h glucose, 2-h insulin, and HOMA-IR before statistical testing to improve the normality. SAS (Version 8.0 for Windows) was used to analyze data. Hardy-Weinberg equilibrium, linkage disequilibrium (LD), and haplotype analyses were carried out using the HelixTree program. Haplotypes were inferred for each individual, and combined haplotypes in a recessive model were also considered.

General characteristics of participants (n = 4,107) by sex and ethnic group (2,763 Chinese, 746 Malays, and 598 Asian Indians) are presented in Table 1. Initially, 147 subjects (77 men and 70 women) were previously diagnosed as having diabetes. From those, 133 were on diabetes medication. Biochemical data (fasting glucose and 2-h glucose) obtained during the study revealed that 245 subjects (105 men and 140 women) could be adjudicated as newly diagnosed diabetic subjects. Thus, 392 participants (182 men and 210 women) were finally classified as diabetic subjects. Both PLIN genotypes were in Hardy-Weinberg equilibrium. Frequencies of the variant alleles were lower in Asian Indians than in the other groups. There was strong (D′ = 0.80, r² = 0.77 P < 0.0001) pairwise LD between PLIN 11482G→A and PLIN 14995A→T across all three ethnic groups.

We first examined the association between PLIN 11482G→A and 14995A→T polymorphisms and diabetes (previously reported and newly diagnosed, n = 392) in the 4,017 participants. We did not find any significant association between these polymorphisms and diabetes (results not shown) or with insulin resistance–related measures (fasting glucose, 2-h glucose, fasting insulin, 2-h insulin, and HOMA-IR) in any ethnic group or sex. As both polymorphisms were in strong LD and yielded similar results, we only show data for the PLIN 11482G→A (Table 2). Haplotype analysis did not change the statistical significance of results (not shown).

We then analyzed whether macronutrient intake modulates the association between PLIN polymorphisms and insulin resistance–related measures in one in every two participants. Subjects with known diabetes were excluded to remove the influence of previously diagnosed disease on dietary patterns. We did not find significant gene–by–ethnic group interactions, and the analyses were done including all the three ethnic groups. Conversely, we found significant interaction with sex, and we analyzed men (n = 1,001) and women (n = 1,153) separately. We observed a recessive effect; therefore, homozygotes and heterozygotes for the wild-type allele were compared with subjects homozygous for the minor allele. Sex-specific tertiles of macronutrient intakes were calculated. In men, we did not find any significant interaction between any one of the PLIN polymorphisms and macronutrients in determining insulin resistance. However, in women we found statistically significant interactions between the PLIN 11482G→A polymorphism and tertiles of total fat, SFAs, and carbohydrate modulating fasting insulin concentrations and HOMA-IR (Table 3). Statistically significant interactions showing similar effects were observed for the PLIN 14995A→T polymorphism (Table 3). Thus, in women homozygous for the less common alleles, HOMA-IR increased as total fat, and specifically SFAs, increased. Conversely, carbohydrate intake was inversely correlated with HOMA-IR among these women (P values for lineal trends were statistically significant only for the 111482G→A polymorphism). Despite some limitations in sample size, due to the low frequency of the GT and AA haplotypes (Table 1), haplotype analysis suggested that PLIN 11482G→A was the most significant marker because no statistically significant interactions in determining insulin resistance were found with total fat, SFAs, and carbohydrate (P = 0.360, P = 0.150, and P = 0.141, respectively) when the haplotype variable was considered. Moreover, no higher effects on insulin resistance were observed in homozygous haplotypes for both polymorphisms (AT/AT) when compared with PLIN 11482G→A homozygotes (AX/AX). Therefore, the 11482G→A variant was considered as the tag polymorphism.

Further adjustment of the SFA interaction by the carbohydrate intake did not modify the statistical significance, revealing independent and additive effects. Then, we simultaneously analyzed intakes of SFAs and carbohydrate by calculating the SFA-to-carbohydrate ratio. The interaction term between this variable (as continuous) and the 11482G→A polymorphism was statistically significant (P = 0.002) in determining HOMA-IR in the whole population (Fig. 1A). Interestingly, in all the three ethnic groups, HOMA-IR increased in women homozygous for 11482A, as the ratio of SFAs to carbohydrate increased. This increase was statistically significant in Chinese (Fig. 1B) and Malays (Fig. 1C), without reaching the statistical significance in Indians (Fig. 1D) because of their lower prevalence of the 11482A allele. When Malays and Indians were combined to increase the statistical power, the interaction term was statistically significant (P = 0.018).

We have found that genetic variation at PLIN locus is an important modulator of the effects of habitual dietary fat and carbohydrate consumption on insulin resistance in a large sample of Asian women from the general population. The association between dietary fat or carbohydrate content and insulin resistance is currently generating a wide degree of public interest and academic debate (17–19). For several decades, medical societies have been promoting high-carbohydrate diets as part of a healthy diet aimed to prevent chronic diseases. More recently, low-carbohydrate diets have been on the rise fostered by the notion that low-carbohydrate, high-fat diets improve insulin resistance in obese patients (18–21); however, there is insufficient evidence to make general recommendations for or against the use of low-carbohydrate diets (18–20). Moreover, the vast majority of studies have not taken into account the genetic characteristics of participants. Therefore, based on the enticing evidence supporting that genetic variation has an important influence in modulating the dietary effect on phenotypic traits (22), it is reasonable to assume that the effects of dietary composition on insulin resistance measures may be different depending on the individual genotype. However, the identification of relevant polymorphisms in candidate genes is not an easy task, as shown by the observed inconsistencies (23,24).

In the present study, we have investigated the PLIN gene because of the relevant physiological role that the adipose tissue plays in insulin resistance (2). Perilipin is the predominant protein associated with lipid storage droplets in adipocytes and one of the critical regulators implicated in the lipid mobilization (4–8). In examining PLIN variation, we have found a statistically significant gene-diet interaction between the PLIN 11482G→A and the PLIN 14995A→T polymorphisms and dietary fat and carbohydrate intake in determining insulin resistance in women. As these polymorphisms were in strong LD, it was difficult to determine their separate effects. However, the higher statistical significance obtained with the 11482G→A polymorphism as well as the results of haplotype analysis suggested that PLIN 11482G→A was the most relevant marker, and we considered this variant as the tag polymorphism.

It is interesting to highlight that the gene-fat interactions were observed only for SFAs but not for MUFAs or PUFAs. This supports a large body of research in animal models and humans suggesting that SFAs clearly worsen insulin resistance, while PUFAs and MUFAs may improve it (25). According to our results, Asian women in the highest SFA tertile had higher HOMA-IR (48% increase) than women in the lowest tertile, if they were homozygotes for the PLIN 11482G→A polymorphism. Conversely, PLIN 11482A homozygous women in the lowest carbohydrate tertile had a 24% higher HOMA-IR score than women in the highest carbohydrate tertile. When combined intakes of SFAs and carbohydrate were considered as a ratio, the differences were magnified. Our results show that the effects of SFA and carbohydrate content in the diet on insulin resistance in Asian women largely depends on the genotype and may contribute to explain the inconsistent results found in different epidemiological studies. Thus, McKeown et al. (26), in the Framingham Offspring cohort, found no association between total carbohydrate intake and HOMA-IR. Conversely, Lau et al. (27) reported that total carbohydrate were inversely related to HOMA-IR in Danish men and women. Several epidemiological studies (28–30) have found that total carbohydrate intake is unrelated to fasting insulin and diabetes risk. Some (31) have suggested that carbohydrate intake is not a sensitive enough measure, and the glycemic index has been proposed as a better measure to classify carbohydrate-containing foods. In the present study, we have not determined the glycemic index, precluding us from examining its interaction with PLIN variation. However, although a high dietary glycemic index has been positively associated with HOMA-IR in some studies, uncertainties regarding its estimation reduce the glycemic index validity in observational studies (32). On the other hand, despite the widespread belief that SFA intake worsens cardiovascular disease risk–related variables (33), inconsistency also exists when the association between SFAs and insulin resistance is examined in epidemiological studies (19,25). In the present study, SFAs were not associated with insulin resistance in the population as a whole. However, high-SFA and low-carbohydrate intakes were strongly associated with higher HOMA-IR in the subgroup of women who were homozygotes for the PLIN 11482G→A polymorphism. Interestingly, this gene-diet interaction was consistently found among Chinese, Malay, and Indian women, despite their different susceptibilities to insulin resistance, increasing the level of causality of this statistical association. In contrast, we did not find statistically significant interactions in men. This finding is not surprising. Previous studies (9–11) analyzing PLIN genetic variation have revealed sex-specific associations in women. Moreover, Mittendorfer (34) examined the evidence regarding sex differences in insulin resistance and concluded that women are intrinsically more insulin resistant than men, possibly because of specific sex-linked gene expression and the resulting differences in metabolic control elements. The biological mechanism underlying this gene-diet interaction remains to be determined; however, some experimental evidence supports our results. Thus, PLIN knockout mice that were resistant to obesity induced by a high-fat diet developed more insulin resistance than control mice (7). In addition, when studying the metabolic adaptation of PLIN-null mice (35), increased β-oxidation and increased insulin resistance with age was observed by another independent group. Although the PLIN 11482G→A polymorphism is located in an intron, Mottagui-Tabar et al. (8) reported that individuals carrying the 11482A allele had significantly reduced perilipin expression in their adipocytes, linking this polymorphism to a metabolic situation similar to that reported in PLIN-null mice.

In summary, we found that Asian women with a moderate degree of insulin resistance and who were homozygotes for PLIN 11482A allele, have increased insulin resistance when consuming a high-SFA, low-carbohydrate diet. This gene-diet interaction was homogeneous among Chinese, Malay, and Indian women, adding consistency to this genetic marker and supporting the notion that dietary recommendations to improve insulin resistance should move forward to more personalized guidelines including relevant genetic information.

Supported by National Institutes of Health/National Heart, Lung, and Blood Grant no. HL54776; contracts 53-K06-5-10, 58-1950-9-001, and 58-3148-2-083 from the U.S. Department of Agriculture; and Grant UV-Estades2005 (to D.C.), Valencia University, Spain.