Epigenetic regulation of PPARGC1A in human type 2 diabetic islets and effect on insulin secretion

Aims/hypothesis Insulin secretion in pancreatic islets is dependent upon mitochondrial function and production of ATP. The transcriptional coactivator peroxisome proliferator activated receptor gamma coactivator-1 alpha (protein PGC-1α; gene PPARGC1A) is a master regulator of mitochondrial genes and its expression is decreased and related to impaired oxidative phosphorylation in muscle from patients with type 2 diabetes. Whether it plays a similar role in human pancreatic islets is not known. We therefore investigated if PPARGC1A expression is altered in islets from patients with type 2 diabetes and whether this expression is influenced by genetic (PPARGC1A Gly482Ser polymorphism) and epigenetic (DNA methylation) factors. We also tested if experimental downregulation of PPARGC1A expression in human islets influenced insulin secretion.
Introduction
Type 2 diabetes is characterised by chronic hyperglycaemia as a result of impaired pancreatic beta cell function and insulin resistance in peripheral tissues, i.e. skeletal muscle, adipose tissue and liver. Although each of these pathogenic defects could be accounted for by specific mechanisms, impaired ATP production as a consequence of reduced oxidative phosphorylation might provide an intriguing common pathogenic pathway for all these defects. The transcriptional coactivator peroxisome proliferator activated receptor gamma coactivator-1 alpha (protein PGC-1α; gene PPARGC1A) is an important factor regulating the expression of genes for oxidative phosphorylation and ATP production in target tissues through coactivation of nuclear receptors [1]. We have previously shown that the expression of PPARGC1A and a set of genes involved in oxidative phosphorylation is reduced in skeletal muscle from patients with type 2 diabetes [2]. Furthermore, a common polymorphism, Gly482Ser, in the PPARGC1A gene has been associated with increased risk of type 2 diabetes and an age-related reduction in muscle PPARGC1A expression [3–5]. In addition, genetic variation in the PPARGC1A gene was associated with indices of beta cell function [6]. Despite the central role of ATP for insulin secretion, the function of PGC-1α in human pancreatic islets and beta cells is less well established [7].
Obesity, reduced physical activity and ageing are well known risk factors for type 2 diabetes. However, all individuals exposed to an affluent environment do not develop the disease. One likely reason is that genetic variation modifies individual susceptibility to the environment. However, the environment could also modify genetic risk factors by influencing expression of a gene by DNA methylation or histone modifications. Cytosine residues occurring in CG dinucleotides are targets for DNA methylation and gene expression is usually reduced when DNA methylation takes place at a promoter. Whether DNA methylation influences gene expression in target tissues for type 2 diabetes and thereby the pathogenesis of the disease remains to be demonstrated.
The present study investigated: (1) whether expression of PPARGC1A is altered in human islets from patients with type 2 diabetes; (2) if this expression is influenced by genetic (the PPARGC1A Gly482Ser polymorphism) and epigenetic (DNA methylation) factors; and (3) if expression of PPARGC1A influences insulin secretion.
Methods
Multi-organ donors The characteristics of the 48 non-diabetic and 12 type 2 diabetic multi-organ donors, whose pancreases were processed for islet preparation, are presented in Table 1. Pancreases were obtained and processed with the approval of the regional Ethics Committee.
Table 1Clinical characteristics of type 2 diabetic and non-diabetic donors Non-diabetic donorsType 2 diabetic donorsp valuen (male/female)48 (30/18)12 (6/6) 9 (7/2)a10 (5/5)a Age (years)53.2 ± 2.466.7 ± 2.4<0.0554.2 ± 3.5a65.1 ± 2.6aBMI (kg/m2)24.8 ± 0.627.1 ± 1.0<0.0525.9 ± 1.3a26.9 ± 1.1aGly/Gly (%)b53.327.3 Gly/Ser+Ser/Ser (%)b46.772.3 Data are expressed as mean ± SEMaDonors used for DNA methylation analysisbPPARGC1A Gly482Ser polymorphism
Human pancreatic islets and experimental plan Isolated pancreatic islets were prepared by collagenase digestion and density gradient purification [8, 9]. After isolation, islets were cultured free floating in M199 culture medium (Sigma-Aldrich, St Louis, MO, USA) at 5.5 mmol/l glucose concentration and studied within 3 days from isolation. Cell viability, measured by Trypan Blue exclusion, was higher than 90% in control and diabetic islets after 3 days in culture.
Insulin secretion study Insulin secretion studies were performed as previously described [8, 9]. Following a 45 min pre-incubation period at 3.3 mmol/l glucose, groups of 30 islets of comparable size were kept at 37°C for 45 min in KRB, 0.5% (wt/wt) albumin, pH 7.4, containing 3.3 mmol/l glucose. At the end of this period, the medium was completely removed and replaced with KRB containing either 3.3, 16.7 or 3.3 mmol/l glucose plus 20 mmol/l arginine, or 3.3 mmol/l glucose plus 100 μmol/l glibenclamide. After an additional 45 min incubation period, the medium was removed. Media (500 μl aliquots from the 10 ml incubation volume) were stored at −20°C until insulin concentrations were measured by immunoradiometric assay (Pantec Forniture Biomediche, Turin, Italy).
Animals Streptozotocin (STZ)–nicotinamide (NA)-treated male Wistar rats (2–3 months old) were administered 210 mg/kg NA i.p. (Sigma, St Louis, MO, USA) dissolved in saline, 15 min before an i.v. injection of 60 mg/kg STZ (Sigma) that had been dissolved in citrate buffer (pH 4.5) immediately before use. Control rats were injected with vehicle alone. STZ–NA-treated animals had stable hyperglycaemia (8.9–10.0 mmol/l) and they were used for the experiments 5 weeks after diabetes was induced. Pancreatic islets were isolated by the collagenase method using the procedure of pancreatic duct cannulation and density gradient purification as described elsewhere [10, 11].
GK rats were obtained from the Stockholm colony and bred as described [12]. Inbred, normoglycaemic F344 rats were purchased from Charles River Laboratories (Wilmington, MA, USA) and maintained by sister–brother mating. Transfer of GK alleles onto the genome of F344 rats by repeated backcrossing (ten generations) established the homozygous congenic strains F344.GK-Niddm1f (NIDDM1F) and F344.GK-Niddm1i (NIDDM1I). NIDDM1F carries 0.5% of the GK genotype (8 cM), based on genetic distance, on a homozygous F344 genetic background. NIDDM1F rats display hyperglycaemia accompanied by fasting hyperinsulinaemia and increased epididymal fat, implicating insulin resistance. NIDDM1I carries 0.8% of the GK genotype (14 cM), and display hyperglycaemia and insulin secretion defects [13–15]. Backcrossing was designed to introduce mitochondrial DNA and chromosomes X plus Y from F344. The congenic strains were kept constant by sister–brother mating for several generations. To avoid effects of the oestrous cycle and other sex-specific influences, only male rats were included in this study. Rats were maintained at constant temperature and humidity in a 12 h light–dark cycle with free access to standard laboratory chow pellets and water. All experiments were approved by the local Ethics Committees. Isolated pancreatic islets were prepared from rats at 8 weeks of age after a 6 h fast (08:00–14:00 hours), by injection of a collagenase solution via the bile–pancreatic duct [16].
Analysis of PPARGC1A mRNA expression in pancreatic islets Total RNA was extracted from human islets, after 3 days in culture, using the RNeasy Protect Mini Kit (Qiagen, Valencia, CA, USA). It was quantified by absorbance at A260/A280 nm (ratio > 1.65) in a Perkin-Elmer spectrophotometer (Waltham, MA, USA) and its integrity was assessed after electrophoresis in 1.0% (wt/wt) agarose gels by ethidium bromide staining. Human and rat PPARGC1A mRNA expression were quantified by RT-PCR [8]. Gene-specific probes and primer pairs for PPARGC1A (Assays-on-demands, human, Hs00173304_m1 and rat, Rn00580241_A1; Applied Biosystems, Foster City, CA, USA) were used. Each sample was run in duplicate and the transcript quantity was normalised to the mRNA level of cyclophilin A (human, 4326316E and rat Rn00574762_A1; Applied Biosystems). For each probe/primer set, a standard curve was generated, which was confirmed to increase linearly with increasing amounts of cDNA.
Downregulation of PGC-1α in human islets using silencing RNA (siRNA) In order to test whether inhibition of PPARGC1A expression could directly modulate insulin release, isolated human islets were transfected with PPARGC1A siRNA by using Arrest-In Transfection (Open Biosystem; Celbio, Pero, Italy). This is a polymeric formulation developed and optimised for highly efficient delivery of siRNA into the nucleus of suspension cells in the presence of serum-containing medium. Pre-designed Silencer siRNAs and Silencer non-targeting siRNAs (negative control) for PPARGC1A (Ambion, Austin, TX, USA) were used. Transfection was performed according to the manufacturer’s instructions. Briefly, islets obtained from five pancreases were washed 12 times in KRB and exposed for 10 min to free Ca2+- and Mg2+-KRB to allow cell disaggregation. At the end of the incubation period, 400 islets per study point were re-suspended in 800 μl M199 medium (Sigma-Aldrich) added with adult bovine serum. siRNA (80 nmol/l) was diluted in 100 μl M199, while 20 μg Arrest-In solution (Celbio) was dissolved in 100 μl M199 and incubated for 10 min after rapid mixing to allow formation of transfection complexes. Finally, 200 μl of this solution were added into wells containing islets and incubation was allowed for 48 h in a CO2 incubator at 37°C. At the end of the incubation period well volume was doubled with M199 culture medium and samples were kept in the incubator for another 48 h, when islets function, viability, transfection efficiency (60% when measured by Polyfectamine tied to a fluorescent probe) and gene expression were evaluated.
Genotyping Genomic DNA was extracted from pancreatic islets using a Wizard Genomic DNA Purification kit (Promega, Madison, WI, USA). The Gly482Ser (GGT→AGT) polymorphism of PPARGC1A was genotyped using an allelic discrimination assay performed with an ABI 7900 system (Applied Biosystems), using PCR primers: 5′-CACTTCGGTCATCCCAGTCAA-3′ (forward) and 5′-TTATCACTTTCATCTTCGCTGTCATC-3′ (reverse), and TaqMan MGB probes: Fam-5′-AGACAAGACCGGTGAA-3′ and Vic-5′-CAGACAAGACCAGTGAA-3′ [5, 17].
DNA methylation A sequence starting 5,000 bp upstream from the PPARGC1A translation start was used in MethPrimer (http://www.urogene.org/methprimer/index.html) to search for regions with CpG sites and PCR designs. A PPARGC1A sequence 986–746 bases upstream from the translation start including four possible DNA-methylation sites and a putative hepatic nuclear factor 1 (HNF-1) binding site, was selected and analysed for DNA methylation (Fig. 1c). Genomic DNA, isolated from pancreatic islets of nine non-diabetic and ten type 2 diabetic multi-organ donors, was treated with bisulphite using the EZ DNA Methylation Kit (Zymo Research, Orange, CA, USA). Bisulphite-modified DNA was amplified by nested PCR with primers designed using MethPrimer. Primer pair 1: 5′-TAGGGTATTAGGGTTGGAATTTAATG-3′ (forward) and 5′-CCCATAACAATAAAAAATACCAACTC-3′ (reverse), and primer pair 2 (used for nested PCR): 5′-TATTTTAAGGTAGTTAGGGAGGAAA-3′ (forward) and 5′-ATAACAATAAAAAATACCAACTCCC-3′ (reverse). The PCR products were then cloned into a vector (TOPO TA Cloning Kit for Sequencing, Invitrogen, Carlsbad, CA, USA) and ten colonies from each donor were purified with a Miniprep kit (Qiagen). These individual clones were sequenced using BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). The number of methylated sites was determined and divided by the total number of methylation sites and then multiplied by 100 to show the percentage of methylation for each donor.
Fig. 1PPARGC1A mRNA expression in human pancreatic islets is influenced by type 2 diabetes, a PPARGC1A Gly482Ser polymorphism and DNA methylation. The influence of a type 2 diabetes and b the PPARGC1A Gly482Ser polymorphism on PPARGC1A mRNA expression in human pancreatic islets. c The PPARGC1A promoter sequence investigated, showing the four DNA methylation target sites; −772, −903, −936 and −961 and a putative binding-site for HNF-1. d The influence of type 2 diabetes on DNA methylation of the PPARGC1A promoter. The influence of e type 2 diabetes (T2D) and f the PPARGC1A Gly482Ser polymorphism on absolute insulin release (pmol islet−1 min−1) in response to 16.7 mmol/l glucose. g Correlations between PPARGC1A mRNA expression and absolute insulin release (pmol islet−1 min−1) in response to 16.7 mmol/l glucose in human pancreatic islets (r = 0.38, p < 0.05). Results are expressed as mean ± SEM. *p < 0.05
Statistical methods Differences in PPARGC1A mRNA expression, percentage of DNA methylation and insulin secretion between the different groups studied were analysed using Student’s t test or the non-parametric Mann–Whitney test, where appropriate. Correlations were calculated using Pearson correlation coefficients for normally distributed values and Spearman correlation coefficients when normality was rejected. Log values were used in the multivariate regression analysis. Differences in expression between GK, F344, NIDDM1F and NIDDM1I rats were analysed using one-way ANOVA, followed by a Kruskal–Wallis Z test. All p values were two-tailed and p values less than 0.05 were considered significant. Statistical calculations were performed by NCSS software (NCSS Statistical Software, Kaysville, UT, USA).
Results
The mRNA expression of PPARGC1A was markedly reduced in pancreatic islets from type 2 diabetic compared with non-diabetic donors (1.06 ± 0.63 vs 15.0 ± 3.0; p < 0.005; Fig. 1a). Intriguingly, non-diabetic carriers of a PPARGC1A 482Ser allele, Gly/Ser or Ser/Ser genotype carriers had markedly lower PPARGC1A mRNA expression compared with carriers of the Gly/Gly genotype [Gly/Ser + Ser/Ser 3.43 ± 0.94 (n = 17) vs Gly/Gly 26.53 ± 4.42 (n = 17); p < 0.00005] (Fig. 1b). However, there was no difference in PPARGC1A mRNA expression between non-diabetic carriers of the Gly/Ser (3.9 ± 4.0; n = 11) or Ser/Ser (2.7 ± 5.5; n = 6) genotypes. This demonstrates that the effect of the genotype on gene expression is seen already in the pre-diabetic state.
The diabetic donors were significantly older and had higher BMI than control donors (Table 1). We therefore tested whether these factors might also relate to PPARGC1A expression in human islets using a multivariate regression analysis including age, BMI and disease status as covariates. Only disease status was significantly associated with PPARGC1A expression in this analysis (r = 1.04; p = 0.0065).
We next evaluated whether epigenetic phenomena such as DNA methylation would influence PPARGC1A expression in islets. Cytosine residues occurring in CG dinucleotides are targets for DNA methylation, and gene expression is usually reduced when DNA is methylated at the promoter. We used bisulphite sequencing to assess DNA methylation of four methylation target sites, −772, −903, −936 and −961 in the PPARGC1A promoter in pancreatic islets from the type 2 diabetic and non-diabetic human multi-organ donors (Fig. 1c). Interestingly, there was an approximately twofold increase in DNA methylation of the PPARGC1A promoter of diabetic compared with non-diabetic human islets (10.5 ± 2.7 vs 4.7 ± 0.9%; p < 0.04; Fig. 1d). Also in this subset of donors, the PPARGC1A mRNA expression was significantly reduced in diabetic compared with non-diabetic islets (p = 0.002). There was a trend towards an inverse correlation between the level of DNA methylation and PPARGC1A mRNA expression (r = −0.48; p = 0.08, after adjustment for disease status).
Insulin release (pmol islet−1 min−1) in response to 16.7 mmol/l glucose was reduced in type 2 diabetic compared with non-diabetic islets (0.32 ± 0.04 vs 0.55 ± 0.05; p < 0.01) as well as in non-diabetic islets carrying the PPARGC1A 482Ser allele (Gly/Ser + Ser/Ser) compared with carriers of the Gly/Gly genotype (Gly/Ser + Ser/Ser 0.43 ± 0.06 vs Gly/Gly 0.64 ± 0.07; p < 0.05; Fig. 1e–f). In addition, the mRNA expression of PPARGC1A correlated positively with glucose-mediated insulin release (r = 0.38; p < 0.05; Fig. 1g). Moreover, the PPARGC1A Gly482Ser polymorphism did not significantly affect basal insulin secretion at 3.3 mmol/l glucose (Gly/Ser + Ser/Ser, 0.23 ± 0.01 vs Gly/Gly, 0.29 ± 0.02; p = 0.13), or insulin response to arginine (Gly/Ser + Ser/Ser 0.48 ± 0.05 vs Gly/Gly 0.53 ± 0.08; p = 0.77) or glibenclamide (Gly/Ser + Ser/Ser 0.59 ± 0.1 vs Gly/Gly 0.56 ± 0.09; p = 0.98).
Having seen the relationship between PPARGC1A expression and insulin secretion, we set out to determine whether modulation of PPARGC1A expression would influence insulin secretion. This was achieved by studying insulin secretion after experimental downregulation of PGC-1α in human islets transfected with PPARGC1A siRNA (n = 3–5 experiments). As shown in Fig. 2, transfection with siRNA was associated with a 45% reduction in the mRNA expression of PPARGC1A (p = 0.01), 32% reduction in insulin mRNA (p = 0.05) and 41% reduction of the insulin stimulation index (i.e. incremental folds above baseline insulin release; p = 0.01). Notably, no significant change was seen in glucagon mRNA expression, suggesting that the effect was primarily due to a downregulation of PGC-1α in beta cells. As a negative control, transfection with scrambled siRNA was not associated with any changes in PPARGC1A (1.26 ± 0.25), insulin (1.29 ± 0.16) or glucagon (0.99 ± 0.15) mRNA expression (p > 0.05).
Fig. 2Transfection of human pancreatic islets with PPARGC1A siRNA is associated with reduced mRNA levels of aPPARGC1A (n = 3) and b insulin (n = 5) and c concomitant reduction of the insulin stimulation index (ISI), i.e. incremental fold change above basal insulin release. d Conversely, inhibition of PPARGC1A expression in human pancreatic islets has no effect on glucagon mRNA expression (n = 5). Results are expressed as mean ± SEM. *p < 0.05
The finding of reduced PPARGC1A expression in human diabetic islets was somewhat surprising given an earlier report of increased PGC-1α levels in islets from diabetic mice (ob/ob) and rat (ZDF and pancreatectomised rats) models [7]. To further explore species-specific differences we also measured PPARGC1A mRNA expression in pancreatic islets from two diabetic rat models. PPARGC1A expression was reduced in islets from STZ–NA diabetic male Wistar rats compared with control animals (3.08 ± 1.07 vs 10.26 ± 1.12; p < 0.009; Fig. 3a). Moreover, PPARGC1A expression was reduced in islets from the congenic NIDDM1I strain, which has insulin secretion defects, compared with the insulin-resistant NIDDM1F strain (7.18 ± 1.79 vs 11.30 ± 1.79; p < 0.02) of the GK rat, an animal model of polygenic type 2 diabetes (Fig. 3b). However, no difference in PPARGC1A expression was found when comparing islets from F344 or GK rat strains (Fig. 3b).
Fig. 3PPARGC1A mRNA expression in rodent pancreatic islets. a Male Wistar rats (2–3 months old) were treated with STZ–NA (n = 5) or vehicles (control; n = 5) [10, 11] and PPARGC1A mRNA expression together with the internal standard cyclophilin A was analysed in pancreatic islets isolated from STZ–NA-treated animals showing a stable hyperglycaemia (8.9–10.0 mmol glucose/l) and controls. bPPARGC1A mRNA expression together with the internal standard cyclophilin A was analysed in pancreatic islets prepared from rats at 8 weeks of age after a 6 h fast. GK rats (n = 6) were obtained from the Stockholm colony and bred as described [12]. Inbred, normoglycaemic F344 (n = 6) were purchased from Charles River Laboratories. Transfer of GK alleles onto the genome of F344 rats by repeated backcrossing (ten generations) established the homozygous congenic strains NIDDM1F and NIDDM1I. NIDDM1F rats (n = 6) carry 0.5% of the GK genotype, based on genetic distance, on a homozygous F344 genetic background (8 cM) and display hyperglycaemia accompanied by fasting hyperinsulinaemia, implicating insulin resistance. NIDDM1I (n = 6) carries 0.8% of the GK genotype (14 cM) and display insulin secretion defects [13–15]. Results are expressed as mean ± SEM. *p < 0.05
Discussion
The key results from the present study were that PPARGC1A mRNA expression and insulin secretion were reduced in pancreatic islets of patients with type 2 diabetes and in non-diabetic carriers of a PPARGC1A 482Ser allele, a genotype previously associated with the disease. Moreover, we demonstrate that epigenetic mechanisms are likely to be operative in the pathogenesis of type 2 diabetes since DNA methylation of the PPARGC1A promoter was increased in human diabetic islets. Experimental downregulation of PPARGC1A expression in human islets by siRNA resulted in decreased insulin secretion thereby demonstrating a causal link between PGC-1α levels and insulin secretion. Based on these observations we propose a model where combinations of genetic and epigenetic factors can influence the level of PGC-1α in human islets and subsequently glucose-stimulated insulin secretion.
The transcriptional coactivator PGC-1α is an important factor regulating the expression of genes for oxidative phosphorylation in a number of tissues including skeletal muscle, liver and adipose tissue. We and others have previously demonstrated reduced expression of PPARGC1A and a set of genes involved in oxidative phosphorylation in skeletal muscle from patients with type 2 diabetes [2, 18]. However, the level and role of PGC-1α in pancreatic islets of patients with type 2 diabetes remained to be determined. The present results indicate that the situation in human islets mirrors the situation in skeletal muscle, since the expression level of PPARGC1A was reduced in human type 2 diabetic islets, thus providing evidence for a common defect that may simultaneously contribute to peripheral insulin resistance and impairment of beta cell function.
In contrast to the present results in humans, elevated PGC-1α levels have been reported in islets from diabetic rodent models, ob/ob mice, ZDF rats and pancreatectomised rats [7]. In these rodent models, the increase in PGC-1α was associated with suppressed beta cell metabolism and insulin release. This could reflect true species differences, but also differences in experimental conditions. To address this issue, we determined: (1) the effect of inhibiting PGC-1α production by transfecting human pancreatic islets with PPARGC1A siRNA; and (2) PPARGC1A expression in pancreatic islets of different rodent models with experimental diabetes, i.e. STZ-diabetic Wistar rats and congenic strains from the type 2 diabetes-like GK rat. Silencing PGC-1α production was associated with concomitant reduction of insulin mRNA expression and insulin release in response to glucose. This effect appears to be highly specific for insulin regulation as it had no effect whatsoever on glucagon mRNA expression. The congenic strains have been bred from the GK rat based upon low insulin secretion or insulin resistance. In line with the results observed in human islets, PPARGC1A expression was reduced in islets from diabetic animals and in rat strains with suppressed insulin secretion. Collectively, these data support the notion that PPARGC1A expression is reduced in animal models of diabetes and human diabetes, and associated with impaired insulin secretion.
The risk of developing type 2 diabetes increases with high-fat/high-energy diets, reduced physical activity and age. However, not all individuals respond to the environment in the same way. This variation in response to environmental factors has partially been ascribed to genetic factors. The common Gly482Ser variant in the PPARGC1A gene is a plausible candidate for such a genetic factor [3, 4]. We have previously shown that this polymorphism is associated with an age-related decline in PPARGC1A gene expression in human skeletal muscle [5]. The present study shows that the same PPARGC1A risk allele, 482Ser, is associated with reduced PPARGC1A expression and impaired insulin secretion in human islets. Thus, our results provide the ground for a common genetic predisposition towards type 2 diabetes contributing to both impaired insulin action and secretion.
Reduced PPARGC1A expression may not simply reflect an effect of the polymorphism per se but could also occur through epigenetic phenomena such as increased DNA methylation of the PPARGC1A promoter. Cytosine residues occurring in CG dinucleotides are targets for DNA methylation, and gene expression is usually reduced when DNA methylation takes place at a promoter. Epigenetic changes are well known to influence gene expression of suppressor genes and oncogenes in cancer cells and contribute to tumour growth [19]. Increased DNA methylation of the promoter of the PPARGC1A gene in type 2 diabetic human islets may contribute to beta cell dysfunction by similar mechanisms. To our knowledge, this is the first study to demonstrate that DNA methylation may play a role in regulating gene expression in human diabetic islets.
Our data suggest that PGC-1α can modulate glucose-mediated insulin secretion in human islets, most likely via an effect on ATP production as indicated by normal insulin release in response to arginine and glibenclamide, which stimulate insulin downstream of ATP. In support of such a role, PPARGC1A mRNA expression correlated with glucose-stimulated insulin release. Moreover, inhibition of PPARGC1A expression by siRNA transfection in human islets was associated with a decline in insulin mRNA and insulin release. Similarly, in human type 2 diabetic islets, reduced PPARGC1A mRNA levels were associated with impaired glucose-mediated insulin secretion. The PPARGC1A Gly482Ser polymorphism, which previously has been associated with type 2 diabetes, was also associated with reduced PPARGC1A expression and impaired insulin secretion in the human islets.
One caveat of this study could be that we did not measure the protein level of PGC-1α in human islets. However, we have previously demonstrated a significant positive correlation between the PPARGC1A mRNA and PGC-1α protein levels in human skeletal muscle [5] and we therefore assume that this might also be the case in human islets. The reduction in PPARGC1A expression in non-diabetic carriers of a 482Ser allele was strong and factors other than haploinsufficiency may therefore explain this reduction. Moreover, whether the genotype influences PPARGC1A expression in diabetic islets would be of interest; however, the present study lacks statistical power to perform this analysis. These relationships should be addressed in future ad hoc studies.
In conclusion, we have shown that PPARGC1A mRNA expression is reduced in islets from patients with type 2 diabetes and it is influenced by both genetic (Gly482Ser) and epigenetic (DNA methylation) factors. This reduction in PPARGC1A expression correlated with impaired glucose-stimulated insulin secretion, which is known to require ATP. Thereby, the present study provides two novel insights into the molecular mechanisms of islet dysfunction in type 2 diabetes; first that a master transcription regulator of oxidative phosphorylation, PGC-1α, may be involved and second, that epigenetic factors like DNA methylation should be considered when searching for the genetic causes of type 2 diabetes.

epigenetic
genetic
ppargc1a
human
type 2 diabetes
pancreatic islets
pgc-1α
dna methylation
gene expression


Regional differences in psychiatric disorders in Chile

Background Psychiatric epidemiological surveys in developing countries are rare and are frequently conducted in regions that are not necessarily representative of the entire country. In addition, in large countries with dispersed populations national rates may have low value for estimating the need for mental health services and programs.
Cross-national psychiatric epidemiological prevalence studies using similar diagnostic instruments have resulted in disparate rates for specific disorders [1]. The reasons for these differences in rates have been attributed to methodological issues between studies; socio-demographic factors such as socio-economic status differences between countries; and cultural differences. Cross-national comparative studies [2, 3] attempt to correct for socio-demographic variability; yet, differences persist. In some regions of the world, such as prevalence rates in Chinese based studies [4–6], the rates are markedly different than the rest of the world. This either suggests true differences in the rates of pathology or lack of cultural appropriateness of the diagnostic tools and the Western diagnostic systems utilized [7]. Understanding these cross-national differences may provide clues into the etiology of psychopathology.
Regional differences also exist within countries as evidenced by studies where the methodology is similar across geographic areas [8–11]. Regional differences in the presence of serious mental illness have been attributed to migration of the mentally ill [12, 13]; birth in urban areas [14]; and genetic pooling [15]. The most studied regional differences are those between urban and rural populations, where urban environmental adversity is argued to contribute to pathology [16, 17].
An understanding of regional differences in countries where the population is spread across large distances is relevant for health care planning. Potentially risk factors may differ across a country resulting in the need to address mental health needs on a regional basis rather than centrally. Furthermore, an understanding of geographic variability permits allocation of resources to be distributed in a proportional basis by need.
This issue is of particular importance to developing countries such as in Latin America, where frequently services are only provided centrally, or disproportionately to the wealthier regions of a country. In addition, epidemiological studies based on single regions of the country [18–20] are extrapolated to larger population bases for which they may or may not be representative.
Population studies about psychiatric disorders in Latin America, as well as other developing regions of the world, are rare. They are important, however, for understanding variations in patterns of disorders, underlying determinants, and service needs. Chile, given its rather extraordinary geography provides an important test of variations in disorder rates across a spatially dispersed population, and offers perhaps the best case example of a country where national rates would seemingly have low value for estimating the need for mental health services and programs.
The Chile Psychiatric Prevalence Study (CPPS) was developed to address issues regarding the prevalence and risk factors for mental illness based on a nationally representative sample, and service utilization. Chile has a population of approximately 16 millions people. The country is composed of 51 provinces grouped in 13 regions covering an area spanning 2 million km2 (including Antarctica and Insular Territories) over a length of 8,000 km. The large distances between major population centers resulted in the CPPS being conducted in four regions of the country, Bio Bio, the south-central region containing the second largest city, Metropolitana, the north-central region which includes the capital Santiago, Tarapaca, the north, and Araucania, the south of the country, in order to obtain a representative sampling of the population of the nation. This report focuses on whether regional differences in the prevalence of psychiatric disorders and service utilization, if present, are due to factors other than socio-demographic differences between population centers.
Methods
Sample selection
The CPPS was based on a household stratified sample of people age 15 and older. A more detailed description of the methods used in the CPPS is available in earlier publications [21]. The sample frame was developed to be representative of the nation’s population. Four regions and their most representative province and comunas were selected. These were subsequently subdivided into districts, and then randomly selected blocks. The number of households available on each block was enumerated. The 1992 census of each region was used to determine the number of households required on each block. A list of the inhabitants, age 15 and older, in each household was generated. Using pre-assigned Kish tables (Kish 1965) one person per household was selected from the list to be interviewed.
The survey was conducted by the University of Concepcion, Department of Psychiatry and Mental Health, between July 1992 and June 1999, with each site being completed in the following order based on funding: Bio Bio, Metropolitana, Tarapaca, and Araucania. A total of 2,987 individuals participated in the survey. Response rate did differ by site (χ2 = 11.08, df = 3, P < 0.02) with Metropolitana having the highest non-response rate 12.6% and Tarapaca the lowest 7.5%. A weight was used to account for the probability of the comuna, district, block, household, and respondent being selected. The data was adjusted to the 1992 census of each region based on age, gender, and marital status using a second weight.
Diagnostic assessment
Composite International Diagnostic Interview (CIDI) versions 1.0 and 1.1 [22] were used to generate the diagnoses using well-trained lay interviewers. DSM-III-R [23] diagnostic criteria were employed. A section on health service utilization in the 6-months prior to the interview was also included. The Spanish translation was conducted using the protocol outlined by the World Health Organization (WHO) [24]. A validation study of the Chilean CIDI was found to have kappas that ranged from 0.52 for somatiform disorders up to 0.94 for affective disorders [25] using a sample of patients and volunteers for each CIDI section. After double entry of data and verification for logical inconsistencies diagnoses were generated using the CIDI computer programs for 1.0 and 1.1 [26]. The DSM-III-R diagnoses included in this report are all affective disorders; all anxiety disorders defined as panic disorder, agoraphobia, and generalized anxiety disorder; substance use disorder which does not include nicotine dependence, and any diagnosis. Lifetime and 12-month prevalence rates were examined.
Interviewers and training
Social science university students in their senior year underwent training following the WHO protocol at the University of Concepcion, a WHO CIDI training and reference center. The 64 interviewers received over 80 h of instruction and practice sessions. Each interviewer had to conduct practice interviews with volunteer adult subjects with and without psychiatric disorders selected from local clinics, as well as a pilot interview on an individual in a non-selected household in the community. Approximately 80% of the interviews were audiotaped following the subject’s consent, and 20% randomly reviewed for quality control.
Analysis procedures
The SUDAAN statistical package [27], Taylor series linearization method, was used to estimate the standard errors due to the sample design and the need for weighting. The analysis was conducted using procedures without replacement for non-respondents. The comuna and district selected were used as the defined strata. Chi-square analyses were used to examine the association of disorders and service utilization between regions. Logistic regression was used to adjust for socio-demographic differences across regions accounting for differential rates or service utilization. Additional analyses were conducted to examine urban–rural differences. The logistic regression analyses included gender, marital status, age group, education, and income as potential confounders. All results are presented as weighted data.
Results
The distribution of income and marital status were found to differ across the four regions of the country (see Table 1). The population of Bio Bio had significantly lower incomes than the other regions (χ2 = 67.96, df = 9, P < 0.0001). In addition, Bio Bio had the lowest rate of individuals who were separated or had annulled marriages (χ2 = 25.31, df = 12, P < 0.05). In two of the regions the rural population was under-represented relative to the census. In Bio Bio 22% of population was rural the sample only included 2.7%, and in Araucania 38% of the population was rural and the sample only included 7.7%.
Table 1Socio-demographic characteristics by regionDisordersBio Bio (N = 800)Metropolitana (N = 1363)Tarapaca (N = 306)Araucania (N = 509)χ2dfP%SE%SE%SE%SEGender  Male48.21.046.41.448.91.047.41.53.2330.38  Female51.81.053.61.451.11.052.61.5Age  15–2426.71.624.92.127.32.327.07.47.3150.94  25–3425.41.626.11.427.10.623.24.6  35–4418.21.918.51.121.12.017.52.6  45–5412.11.412.81.211.80.412.31.2  55–649.01.49.20.86.50.49.81.3  65+8.72.38.51.06.21.910.41.7Education  No education2.71.01.10.20.30.21.31.011.1390.31  Basic19.73.817.92.37.82.015.27.6  Medium52.33.347.91.652.71.538.27.3  High25.27.133.22.339.13.245.215.4Marital status  Married54.72.852.82.353.73.252.35.825.31120.05  Widowed5.21.14.70.73.31.35.80.2  Separated/anulled1.70.74.10.53.31.42.00.6  Never Married34.41.932.62.433.91.136.18.0  Common Law4.00.85.81.05.90.63.82.0Income  U$100–U$40070.57.154.53.912.24.847.715.467.9690.0001  U$401–U$80019.33.321.91.829.62.520.64.5  U$801–U$15006.91.911.91.335.63.312.54.0  U$1501+3.32.511.72.822.73.619.27.2Urban/rural  Urban97.33.199.20.998.91.492.35.03.130.39  Rural2.73.10.80.91.11.47.75.0
Prior to adjusting for socio-demographic differences between the regions, a number of differences in prevalence rates were noted (see Tables 2, 3). Lifetime rates for major depressive disorder were markedly elevated in Tarapaca, 17.2%, and lowest in Bio Bio, 11.6% (χ2 = 9.76, df = 3, P < 0.04). Drug abuse, but not dependence, also had the highest prevalence rate in Tarapaca, 2.4% (χ2 = 8.59, df = 3, P < 0.05). Interestingly, in Araucania the rate of non-affective psychosis was the lowest (χ2 = 11.45, df = 3, P < 0.02). When females were examined the differential rates for major depressive disorder (χ2 = 11.76, df = 3, P < 0.02) and substance use disorders were noted (χ2 = 10.88, df = 3, P < 0.03). Among males the only lifetime difference in prevalence rates was for elevated alcohol abuse in Tarapaca (χ2 = 9.17, df = 3, P < 0.04). For 12-month prevalence the increased risk for major depression in Tarapaca persisted (χ2 = 8.78, df = 3, P < 0.05) for both genders combined and for females (χ2 = 10.08, df = 3, P < 0.03). The differences noted in the prevalence of substance use disorders were no longer evident at 12-months.
Table 2Lifetime prevalence rates of DSM-III-R disorders by regionBio BioMetropolitanaTarapacaAraucaniaDisorders%SE%SE%SE%SEχ2PAffective disorders    Major depressive episode7.11.311.60.817.22.49.80.89.760.04    Manic episode2.20.71.40.41.80.31.51.31.110.78    Dysthmia7.51.17.31.212.21.76.03.33.650.32    Any affective disorder13.62.215.41.223.21.914.03.23.880.30Anxiety disorders    Panic disorder1.20.61.30.34.30.81.10.51.160.77    Agoraphobia without panic14.22.79.81.39.71.85.30.85.460.16    Generalized anxiety disorder1.80.73.70.52.00.43.00.65.130.19    Any anxiety disorder19.23.714.81.617.91.78.60.96.790.11Substance use disorders    Alcohol abuse2.80.82.00.516.92.67.21.48.200.06    Alcohol dependence7.01.76.40.86.30.55.00.83.630.32    Drug abuse0.60.31.50.42.40.50.10.18.590.05    Drug dependence2.20.73.31.02.20.31.20.17.720.07    Nicotine dependence2.90.62.10.66.50.25.41.07.860.07    Any alcohol or drug use disorder11.01.811.21.024.32.912.31.81.940.59    Any substance use disorder13.02.012.91.129.62.914.61.72.430.50Other disorders    Non-affective psychosis2.10.62.30.50.80.20.10.011.450.02    Somatoform disorder2.71.14.40.83.10.63.50.71.750.63    Cognitive disorder4.51.83.70.80.70.81.00.45.700.15Any CPPS disorder32.24.130.81.644.41.928.93.12.510.49FemaleAffective disorders    Major depressive episode7.11.315.21.420.60.811.21.111.760.02    Manic episode2.61.01.80.53.00.60.50.53.620.32    Dysthmia10.72.011.61.917.21.38.75.12.430.50    Any affective disorder16.42.621.01.728.30.916.04.93.600.32Anxiety disorders    Panic disorder1.70.92.10.56.71.22.10.91.150.77    Agoraphobia without panic20.33.213.62.012.20.27.02.44.880.20    Generalized anxiety disorder2.51.06.41.03.80.84.40.45.980.13    Any anxiety disorder26.43.920.92.524.82.112.32.05.190.18Substance use disorders    Alcohol abuse0.50.41.10.62.60.41.00.42.060.57    Alcohol dependence1.70.72.00.74.20.20.40.27.530.07    Drug abuse0.00.00.80.32.30.50.00.07.380.08    Drug dependence2.40.75.01.62.70.40.20.26.860.10    Nicotine dependence2.50.72.30.810.00.62.60.42.450.49    Any alcohol or drug use disorder3.60.97.11.56.90.61.60.611.800.02    Any substance use disorder5.91.48.91.416.50.34.10.710.880.02Other disorders    Non-affective psychosis2.40.71.90.61.60.30.10.17.810.07    Somatoform disorder2.40.75.31.13.60.74.41.74.100.27Any CPPS disorder35.63.834.72.537.81.123.44.03.020.40MaleAffective disorders    Major depressive episode7.11.57.41.313.64.68.31.56.840.10    Manic episode1.81.01.00.40.50.62.62.21.180.76    Dysthmia4.01.02.40.67.13.33.12.04.100.27    Any affective disorder10.52.29.01.418.04.311.82.914.570.01Anxiety disorders    Panic disorder0.70.50.40.21.80.30.00.04.390.24    Agoraphobia without panic7.72.95.31.97.23.53.51.03.940.28    Generalized anxiety disorder1.00.70.70.40.00.01.31.01.830.61    Any anxiety disorder11.54.47.91.710.63.44.50.57.590.07Substance use disorders    Alcohol abuse5.31.53.00.831.95.614.12.89.170.04    Alcohol dependence12.53.711.61.98.41.210.21.81.110.77    Drug abuse1.10.72.40.82.60.60.20.26.930.09    Drug dependence1.91.11.50.61.60.42.30.51.400.71    Nicotine dependence3.20.71.90.92.90.58.62.26.050.13    Any alcohol or drug use disorder18.94.115.91.942.66.324.33.44.830.20    Any substance use disorder20.54.317.62.143.35.826.22.95.550.16Other disorders    Non-affective psychosis1.81.12.71.10.00.00.00.05.780.14    Somatoform disorder3.01.73.21.32.60.52.60.90.230.97Any CPPS disorder28.55.326.42.351.24.035.05.04.460.23Non-affective psychosis includes schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder, and atypical psychosisAny CPSS disorder does not include nicotine dependence or cognitive disorder; χ2 df = 3Table 312-Month prevalence rates of DSM-III-R disorders by regionBio BioMetropolitanaTarapacaAraucaniaDisorders%SE%SE%SE%SEχ2PTotalAffective disorders    Major depressive episode4.10.97.80.810.31.95.11.18.780.05    Manic episode1.90.71.10.31.80.31.00.92.050.57    Dysthmia3.00.74.11.27.92.23.12.26.100.13    Any affective disorder7.81.610.81.315.01.67.52.95.190.18Anxiety disorders    Panic disorder0.50.30.40.24.10.90.60.61.320.73    Agoraphobia without panic7.01.86.11.57.52.02.20.94.440.24    Generalized anxiety disorder1.20.52.30.61.10.31.70.52.410.50    Any anxiety disorder11.11.79.21.611.91.63.81.35.690.15Substance use disorders    Alcohol abuse2.10.61.80.55.81.83.30.84.050.28    Alcohol dependence5.21.54.80.73.70.42.80.54.260.26    Drug abuse0.20.20.40.21.50.40.00.03.990.28    Drug dependence1.50.81.90.51.80.30.40.42.910.42    Nicotine dependence2.10.51.90.65.60.34.41.04.180.26    Any alcohol or drug use disorder8.41.67.90.910.72.06.21.22.840.43    Any substance use disorder10.01.69.41.015.72.18.21.62.380.51Other disorders    Non-affective psychosis1.10.41.40.30.80.20.00.08.450.06    Somatoform disorder1.80.83.90.63.10.63.00.62.950.42Any CPPS disorder23.93.523.01.525.31.714.73.92.840.43FemaleAffective disorders    Major depressive episode4.80.910.51.314.80.85.32.410.080.03    Manic episode2.61.01.50.53.00.60.20.26.250.12    Dysthmia4.31.46.92.38.81.73.93.26.920.09    Any affective disorder9.61.815.22.220.00.98.25.35.720.15Anxiety disorders    Panic disorder0.50.30.60.36.61.31.01.21.370.71    Agoraphobia without panic11.93.08.42.410.40.33.41.05.780.14    Generalized anxiety disorder1.40.83.91.02.20.62.70.73.240.37    Any anxiety disorder17.32.313.02.517.51.26.31.75.900.14Substance use disorders    Alcohol abuse0.50.41.00.50.90.20.60.50.930.82    Alcohol dependence0.90.51.30.74.20.20.00.05.160.18    Drug abuse0.00.00.20.20.50.10.00.02.480.49    Drug dependence1.50.72.90.82.70.50.20.28.500.05    Nicotine dependence2.00.72.10.88.50.42.00.81.710.64    Any alcohol or drug use disorder2.91.04.50.85.10.40.80.77.980.06    Any substance use disorder4.91.46.00.613.30.92.80.714.740.01Other disorders    Non-affective psychosis1.50.71.20.51.60.30.00.07.050.09    Somatoform disorder1.90.75.01.13.60.74.21.84.500.23Any CPPS disorder23.42.724.72.928.40.912.35.12.830.42MaleAffective disorders    Major depressive episode3.41.14.71.15.53.65.00.41.300.73    Manic episode1.10.90.70.30.50.61.81.60.680.88    Dysthmia1.60.80.80.37.03.32.12.04.780.21    Any affective disorder5.91.75.71.19.82.86.71.34.460.23Anxiety disorders    Panic disorder0.60.40.20.21.60.40.00.03.070.39    Agoraphobia without panic1.70.93.41.74.53.80.90.91.940.59    Generalized anxiety disorder1.00.60.40.20.00.00.50.41.970.58    Any anxiety disorder4.41.84.71.66.13.51.10.93.260.37Substance use disorders    Alcohol abuse3.71.22.70.810.93.76.31.26.200.12    Alcohol dependence9.72.88.81.83.11.05.90.93.880.29    Drug abuse0.40.40.70.52.60.60.00.03.470.34    Drug dependence1.51.20.80.50.90.20.60.80.330.95    Nicotine dependence2.20.71.80.82.50.57.02.03.080.39    Any alcohol or drug use disorder14.43.211.82.016.64.412.11.61.250.74    Any substance use disorder15.43.213.32.118.24.014.12.81.070.78Other disorders    Non-affective psychosis0.80.41.60.70.00.00.00.05.750.14    Somatoform disorder1.61.22.61.02.60.51.80.82.050.57Any CPPS disorder21.85.320.52.522.03.017.33.41.120.77Non-affective psychosis includes schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder, and atypical psychosisAny CPSS disorder does not include nicotine dependence or cognitive disorder; χ2 df = 3Table 4Mental health service utilization in the past 6-months by region among those with DSM-III-R 12-month prevalent disorderBio BioMetropolitanaTarapacaAraucaniaDisorders%SE%SE%SE%SEχ2PAny MH service17.61.121.12.525.90.312.01.910.090.03Non-specialized MH service15.21.017.11.824.30.410.71.612.630.02Specialized MH service5.20.96.71.35.61.13.41.14.260.26Substance service0.10.10.30.20.40.10.10.12.870.43χ2 df = 3, MH = Mental HealthNon-Specialized MH Services = primary care physicians; Specialized Mental Health Services = inpatient or outpatient services provided by a psychiatrist or a psychologist or formal substance abuse services; Substance abuse services included inpatient and outpatient detoxification or Alcoholics Anonymous
Using logistic regression controlling for socio-demographic variables the regional differences for major depression were maintained for both lifetime and 12-month prevalence, as well as among females in both prevalence periods. Males with lifetime prevalent affective disorders were also at increased risk in Tarapaca. In addition regional differences in lifetime prevalence for alcohol abuse, drug abuse, and nicotine dependence were found. Among females, regional differences in lifetime prevalence were noted for drug abuse, drug dependence, any alcohol or drug use disorder, and cognitive disorders, and among men alcohol abuse with increased risk among those residing in Tarapaca. The statistical differences in regional lifetime prevalence of non-affective psychosis for both genders combined, and females in both prevalence periods, persisted with the rates for Araucania remaining low. As the rates for not only major depression, but also alcohol and drug use disorders were elevated in Tarapaca, additional analyses were conducted controlling for comorbidity in the logistic regressions, the regional differences noted were not altered.
Differences in service utilization across the four regions were also found. Araucania had the lowest use of mental health services utilization (χ2 = 0.03, df = 3, P < 0.03), in particular in the non-specialized health care sector (χ2 = 12.63, df = 3, P < 0.02). The rates of service utilization by region are presented in Table 4. The lower use of services persisted after controlling for socio-demographic variables in a logistic regression.
When urban versus rural was examined across all sites no statistical differences in the rates of disorders were noted. In addition, there were no socio-demographic differences. The sample size of the rural population was small, 203.
Discussion
Regional differences that persisted after adjusting for potential confounders persisted in the CPPS. Major depression and substance use disorders were highly prevalent in Tarapaca. The high rates of substance use disorders, especially drugs, and were not surprising as the region bordering Bolivia and Peru is heavily involved in the drug trade. The increased rates of major depression, however, could not be accounted for by substance use disorder comorbidity. The differences in rates for non-affective psychosis, although may simply be due to a type 1 error, are nonetheless surprising as the Araucania region’s population and our sample has a sizable proportion of Mapuche indigenous people. The Mapuche in earlier psychiatric literature were thought to be at increased risk for psychosis [28]. The small sample size of the rural population precluded finding statistically significant differences.
The utilization of health service was lowest in Araucania and Bio Bio. This may be consistent with the inequities in availability and access that do exist in health and mental health resources across different regions of Chile. The southern half of the country is the poorest and has the least resources; therefore, the lower rates may be due to a lack of access rather than demand. For example across the regions the number of available mental health beds 2001 in the public health service per 100,000 were Araucania 2.2; Bio Bio 4.8; Metropolitana 34.2; and Tarapaca 47.2. The number of primary care physicians per 100,000 populations also was lowest in Araucania, 57.0, compared to Bio Bio with 169.7, Tarapaca with 61.6 and Metropolitana with 185.8.
It could be argued that these regional differences are simply artifact due to sampling differences. Clearly the population investigated in Tarapaca is small for a prevalence study and may have resulted in rates that may prove unstable. Another potential limitation is that the four regions were investigated sequentially, with the potential for socio-cultural influences to impact on the rates during the intervals between data collection. The high proportion of low-income individuals in the Bio Bio sample in comparison to the other sites and in particular Aracucania, the poorest region of the country, reflects the improved economic conditions in Chile during the course of data collection and supports a cohort effect. A cohort effect, however, is highly unlikely to explain the rates of psychopathology given that data collection was obtained from Araucania last, yet it has the highest rate of major depressive disorder.
Conclusion
Regional differences across countries may exist that have both implications for prevalence rates and service utilization. Planning mental health services for population centers that span wide geographical areas based on studies conducted in a single region may be misleading, and may result in areas with high need being underserved. Psychiatric epidemiological studies that are nationally representative of developing nations are needed that have a sufficient sampling frame to examine populations believed to be at high risk and regions where increased inequities may exist. Even the most recent epidemiological studies representing Latin America [6, 29] have ignored large segments of the population, such as those countries and regions of countries with large indigenous populations or segments of the population that are very poor. Fewer studies in the region have examined service needs and none have addressed regional differences in services. Data that is more representative of the Latin American population is needed in order to improve mental health services planning and addresses the large under-estimated treatment gap.

regional differences
psychiatric disorders
chile
community prevalences


Temperament as a predictor of internalising and externalising problems in adolescent children of parents diagnosed with cancer

Objective This study examined the relationship between temperament and internalising and externalising problems among children of parents diagnosed with cancer, beyond the effects of socio-demographics, illness-related variables and life events.
Introduction
All people are confronted with intense experiences during the course of their lifetime. Even children are not spared from them. Adolescent children, especially, are vulnerable to stressful events [1]. One stressful incident with which families with adolescent children are increasingly confronted is the diagnosis of cancer in one of the parents. Studies have shown that adolescent children in such situations, the daughters particularly, experience more emotional problems than their age peers do [2, 3]. Although the functioning of children of cancer patients has received increasing attention, research into risk and resilience factors is still in its infancy. Recent studies found evidence for the impact of parental characteristics [4], parent–child communication [5] and family functioning [6] on child functioning. Increased attention is given to the different effect of parental cancer for boys and girls and children of different ages, but research into other child characteristics is lacking. Temperament is one of the factors that may have an important influence on how children cope with stressful events, and therefore why some children are more vulnerable to the development of problems than others [7]. The primary hypothesis of most models of temperament is that specific dimensions are related to prevalence of specific problems. Research into the role of temperament can provide insight into which children are at greater risk in such situations and which children are better equipped to handle the situation.
The developmental model of temperament as proposed by Rothbart and Derryberry is one of the few models that offers a measurement tool that is specifically designed for use with adolescents, and it was therefore used in the present study. Rothbart proposes that temperament is relatively stable, but that the expression of temperament can change as a result of maturation and (social) environmental influences, including life events [8, 9]. The model distinguishes between two concepts: reactivity and self-regulation. Reactivity is comprised of the physical and emotional differences that exist among individuals in reaction to stress. Children who have a high degree of reactivity are more easily upset and need more time to recover than other children do. The term “self-regulating system” refers to such processes as attention, activation and inhibition. These processes have an important influence on the regulation of emotions and behaviour. These children may be able to direct attention away from the stressor, such as parental cancer.
To our knowledge, no studies have reported on the effect of different temperament dimensions on the functioning of children of parents who have been diagnosed with cancer. The aim of the current study was to investigate the effects of temperament on prevalence of problems in these children, beyond the possible effects of socio-demographics (age, gender, educational level), illness-related variables (recurrent disease and time since diagnosis) and the number of negative life events that children experienced during the year before assessment.
Materials and methods
Procedure
Between January 2001 and February 2003, written information on this study was offered to all cancer patients who were consecutively hospitalised or who visited the outpatient’s clinic at the University Medical Centre Groningen by their physicians or oncology nurses. In addition, information was sent to patients and their family members who had contacted the researchers in response to media attention because they wished to participate in the study. Families were eligible if patients had been diagnosed between 1 to 5 years before study entry and if they had children between 4 to 18 years of age. Participants had to be fluent in Dutch. Patients discussed study participation with their partners (if present) and children. Informed consent was obtained according to the regulations of the Medical Ethical Committee of the University Medical Centre Groningen. After informed consent was received, questionnaires and prepaid return envelopes were provided.
Participants were guaranteed that answers were treated absolutely confident and will be described completely anonymous.
Participants
The current study is part of a larger study in which 476 families with children aged 4 to 18 years were approached, and information was mailed to 110 families who had contacted us for information about participation. Of these, 205 families from the first group and 89 families from the second group consented to participate (response: 43 and 81%, respectively). Ill parents who did not participate did not significantly differ from those who participated with respect to gender, tumour type and time since diagnosis.
The current study focused on the responses of children of 11 years of age and older, as they completed the questionnaires themselves. The sample for the present study consisted of 340 adolescent children (149 sons and 191 daughters), between the ages of 11 and 18 years (mean age=14.9 years, SD=2.3) and their 212 ill parents (80% mothers, mean age=45.4 years, SD=4.7). Twenty-one percent of the children were receiving education in primary school, 9% at the lower vocational level, 17% in lower general secondary education, 12% in intermediate vocational education, 38% in high school and 3% in higher vocational education or university. Nine percent of the children were from single-parent families. Parents (43 fathers; 169 mothers, mean age=45.4 years, SD=4.7) had been diagnosed with various types of cancer: breast (55%), haematological (9%), skin (9%), gynaecological (9%), urological (5%), bone tumours (4%), gastrointestinal (5%) or other cancers, such as cancer of the central nervous system or head and neck cancer (6%). The mean time since diagnosis was 2.6 years (SD=1.2). Twenty-nine percent of the parents had suffered relapses.
Children and parents approached in the hospital did not significantly differ in age or gender from children and parents who had volunteered for participation. Educational level of parents of the last-named group was significantly higher (t=5.8, p≤0.001) than that of the first-named group, but this was not found for children’s educational level. Furthermore, children and parents in both groups reported similar levels of internalising and externalising problems, and children did not differ significantly in temperament.
Measures
Temperament Temperament was measured using the adolescent version of the Revised Early Adolescent Temperament Questionnaire (EATQ-R) of Rothbart and Derryberry [10–12]. The EATQ-R consists of 53 items and includes ten subscales that are designed to measure temperamental attention control, activation control, inhibitory control, high intensity pleasure, shyness, fear, frustration, affiliation, perceptual sensitivity and pleasure sensitivity. Answers were rated on a 5-point Likert-type scale (1 = “almost always untrue” to 5 = “almost always true”). Higher values represent a higher availability of the temperamental dimension concerned. The psychometric quality of the EATQ-R was reported to be sufficient among American adolescents with Cronbach’s alpha’s ranging from α=0.55 to α=0.78 [13]. In the present study, alpha scores ranged from α=0.36 to α=0.74, and mean inter-item correlations from r=0.11 to r=0.37. Five of the ten dimensions had mean inter-item correlations of <0.20. Therefore, factor analyses were carried out to study the extent to which the temperamental dimensions identified by Rothbart and colleagues emerged from the data of the present Dutch study (see preliminary analyses).
Internalising and externalising problems The Youth Self-Report (YSR) and Child Behaviour Checklist (CBCL) were used to assess, respectively, adolescent children’s self-reported and parent’s reported behavioural and emotional problems in children [14–16]. The YSR/CBCL consists of 102/120 problem items with three response options (0 = not true, 1 = somewhat or sometimes true and 2 = very true or often true). Higher scores indicated more problems. The Internalising (TIS) and Externalising Behaviour Problem Scales (TES) were used in the present study. The internalising scale (32 items) consists of the syndrome subscales withdrawal, somatic complaints and anxiety/depression. The externalising scale (30 items) consists of the syndrome subscales delinquent behaviour and aggressive behaviour. The YSR is one of the most commonly used questionnaires in adolescent research. Cronbach’s alpha scores in the present study were high (α>0.80). The manual provides norm data based on a randomly selected Dutch sample of 560 adolescent boys and 564 adolescent girls.
Life events The Questionnaire of Recently Experienced Events was used to ask children and parents about the number of life events experienced during the past year. This questionnaire is based on the Recent Life Change Questionnaire (RLCQ) developed by Rahe [17]. Questions measuring negative events (14 items; e.g., divorce, illness by other family members than the parent with cancer) were used.
Analyses
Factor analyses of the EATQ-R were executed using Simultaneous Confirmatory Analysis (SCA) and Exploratory Principal Component Analysis (PCA) to investigate factor validity.
Chi-square and t tests were performed to compare children and parents who had been recruited in the hospital and those who had volunteered for participation on demographic characteristics and the problems reported.
One-sample t tests were performed to compare prevalence of internalising and externalising problems reported by children and parents diagnosed with cancer with those of the norm group.
Univariate statistics (t tests and pearson correlation analyses) were performed to investigate effects of study variables (age, gender, educational level, time since diagnosis, recurrence, number of negative life events, and temperament) and problems in children.
Hierarchical regression analyses were conducted to examine the contribution of temperament dimensions to prediction of internalising and externalising problems in children. Socio-demographics and illness-related variables (first step), and number of life events (second step) significantly related to temperament were entered into regression analyses to ensure that any effect found for temperament (third step) on children’s problems would not be attributed to these variables. To examine whether multicollinearity exists between the independent variables, Pearson correlation analyses and Variance Inflation Factors (VIF) were performed. If the mean VIF is considerably larger than one and the largest VIF is greater than 10, multi-collinearity exists [18].
There is evidence that sons and daughters may respond differently to the cancer in the parent [19] and that they differ in temperament [13, 20]. Two-way-interaction terms (using standardised scores) were computed to examine whether the pattern of the relationship between temperament and prevalence of problems differ between sons and daughters. Only when the 2-way interaction accounted for a unique significant effect was it included in the model.
Owing to the large number of comparisons in relation to the sample size significance was set at p≤0.01.
Results
Preliminary analyses
Simultaneous Confirmatory Analysis (SCA) was conducted to examine differences in the percentage of variance explained by the original structure and by the exploratory structure over the same number of factors. The difference in the variance explained by the original structure (41.0%) and by the exploratory structure (46.1%) was considered too large (>2% rule of thumb, [21]) to continue with the original structure. An exploratory Principal Component Analysis (PCA) was conducted using orthogonal rotation followed by varimax procedure. The number of constituting factors was determined based on the scree test. Items that loaded consistently low (<0.30) or on varying components were excluded. In addition, all items—particularly those with loadings between 0.30 and 0.40—were critically examined for the degree to which they formed a good reflection of the dimensions on which they loaded (face validity). Dimensions were assessed as consistent by an alpha above 0.60 and an inter-item correlation above 0.20. On the basis of these criteria, 17 items were excluded, including the entire inhibition-control (five items) and the affiliativeness scale (five items). Thirty-six items remained, distinguishing seven temperamental dimensions. The first dimension was called effortful control and consists of all five items from activation control and four of the six items from attention control. One of the attention control items was excluded and the other was added to the original four perceptual sensitivity items (“I am good at keeping track of several things that are happening around me”) that represent the second dimension. The third dimension is pleasure sensitivity; just one item did not meet the criteria and was excluded. The fear/worry dimension forms the fourth dimensions. Three of the six items loaded on this scale. One of the other items was excluded, one was added to pleasure intensity (“I get frightened when I ride with a person who likes to speed”), and one was added to the original four items of the shyness scale (“Some of the kids at school make me nervous...”). Shyness was the fifth dimension. The sixth dimensions form the pleasure intensity. Three of the six items loaded low or were not consistent, and were therefore excluded. The seventh dimension forms frustration. One of the seven items did not meet the criteria and was excluded. The seven temperamental dimensions can be defined as follows. Effortful control measures the capacity to start and persist in an action and to focus attention. Pleasure sensitivity is the pleasure related to stimuli involving low intensity, complexity, novelty and incongruity. Perceptual sensitivity pointed to detection or perceptual awareness of slight, low-intensity stimulation in the environment. Pleasure intensity represents the pleasure derived from activities involving high intensity. Shyness is the behavioural inhibition to novelty and challenge, especially social. Frustration measures the negative effect related to interruption of ongoing tasks or goal blocking. Fear/worry represents the worry for occurrence of unpleasant situations. The seven factors that were formed of remaining items explained 47.2% of the variation. Alpha coefficients from the other dimensions ranged from α=0.61 to α=0.79, and inter-item correlations ranged from r=0.21 to r=0.48 (Table 1).
Table 1Cronbach’s alpha EATQ-R dimensions of children in the current study and a control studyDimensionsCurrent studyControlCronbach’s alphaMean inter-item correlationsCronbach’s alphaMean inter-item correlationsNumber of itemsEffortful control0.750.260.740.259Pleasure sensitivity0.790.360.830.564Shyness0.690.300.630.255Frustration0.610.210.690.276Perceptual sensitivity0.620.250.700.315Pleasure intensity0.710.370.740.424Fear/worry0.610.340.610.353
Cross validity The same research group conducted a prospective study among children of parents recently diagnosed (1–16 weeks ago) with cancer using a similar procedure in approaching families as was used in the current study. The adapted structure was tested among 144 adolescent children (54% daughters; mean age=14.2 years, SD=2.3 years) that participated in the prospective study (comparison group). Results of PCA among children in the prospective study were similar to those found in the current study. The internal consistency and mean inter-item correlations of the two groups were comparable (Table 1). Results of the Principal Components Factor Analysis are available on request.
Internalising and externalising problems in children
T tests revealed no significant differences in prevalence of internalising and externalising problems between sons and norm group boys as reported by themselves and their ill parents. Daughters and their ill parents reported significantly more internalising problems than found in norm group girls (Table 2).
Table 2Descriptive statistics of the YSR and one sample t tests for comparison of study and norm group SonsNorm group boystDaughtersNorm group girlstMeanSDMeanSDMeanSDMeanSDInternalising problems YSR9.67.38.65.81.613.99.910.87.14.3*Externalising problems YSR11.46.211.56.7−0.210.86.210.06.11.7Internalising problems CBCL6.16.25.75.70.88.47.26.56.03.5*Externalising problems CBCL6.66.17.17.1−0.95.96.05.55.80.9*p<0.001
Children’s problems and socio-demographics, illness-related variables and life events
Socio-demographics Daughters experienced significantly more internalising problems than sons as was reported by ill parents and self-reports (t=2.9, p=.004; t=4.8, p≤0.001). Neither children’s age and educational level, nor parent’s age, gender or educational level were significantly related to the prevalence of problems reported in children.
Illness-related variables Parents and children reported more internalising problems in case of recurrent disease than in case of primary disease. Time since diagnosis was not significantly associated with reports of either internalising or externalising problems (even after controlling for recurrent disease).
Life events The number of negative life events children or parents experienced during the preceding year was significantly positively correlated with internalising (r=0.36, p≤0.001, r=0.25, p≤0.001, respectively) and externalising problems (r=0.25, p≤0.001) reported by or in children.
Children’s temperament and socio-demographics, illness-related variables and life events
Socio-demographics Sons had significantly higher mean scores on pleasure intensity (t=4.7, p≤0.001) and significantly lower scores on shyness (t=−2.8, p=0.005) than daughters did. Age of children was significantly negatively related to effortful control (r=−0.18, p≤0.001). No relationship was found between child’s educational level and temperament.
Illness-related variables No significant relationships were found between length of time since diagnosis and children’s temperament. Children of parents who had recurrent disease differed significantly in fear/worry from children of parents with primary disease (t=3.1, p=0.002).
Life events Number of negative life events experienced was significantly positively correlated with pleasure sensitivity (r=0.15, p=0.007), perceptual sensitivity (r=0.15, p=0.005) and fear/worry (r=0.30, p≤0.001) and negatively with effortful control (r=−0.15, p=0.008).
Relationships between temperament and problems in children
All temperament dimensions were significantly related to children’s self-reported internalising problems. Shyness, pleasure intensity and fear/worry were significantly related to internalising problems as reported by ill parents. Effortful control, frustration and fear/worry were significantly associated with the prevalence of externalising problems in children as reported by children and parents (Table 3).
Table 3Descriptive statistics of the temperament dimensions and correlations of these variables with internalising and externalising problemsTemperamentMeanSDChildrenIll parentsInternalisingExternalisingInternalisingExternalisingrrrrEffortful control27.86.2−0.25**−0.44**−0.05−0.24**Pleasure sensitivity11.24.022**0.030.13−0.04Shyness12.03.80.30**0.000.19**−0.13Frustration18.43.80.29**0.36**0.100.14*Perceptual sensitivity16.43.50.15*0.07−0.01−0.09Pleasure intensity14.73.7−0.18**−0.02−0.19**0.10Fear/worry7.42.70.46**0.29**0.23**0.16**p<0.01**p<0.001
Predictors of internalising and externalising problems
Internalising problems Child’s gender and recurrent disease explained a significant percentage of the variance in the prevalence of internalising problems in child’s (R2Ch=0.10) and parent’s reports (R2Ch=0.08). Both variables appeared to have a significant negative independent effect. Life events accounted for a significant increment in explained variance in child’s (R2Ch=0.10) and parent’s reports (R2Ch=0.07). The temperament dimensions predicted a significant percentage of additional variance in children’s (R2Ch=0.27) and parent’s reports (R2Ch=0.04). Shyness, frustration, perceptual sensitivity and fear/worry appeared to have a significant positive independent effect in children’s reports. None of the variables appeared to have a significant independent effect in parent’s reports. Interaction terms between temperament dimensions and child’s gender failed to contribute significantly to the prediction of internalising problems in children and parent’s reports. The final models accounted for 48 and 19% of the variance in child’s and parents’ models, respectively (Table 4). VIFs ranged between 1.0–1.3, suggesting that there was no problem of collinearity.
Table 4Regression analyses examining temperament as a predictor of internalising problems reported by children an ill parents ChildrenIll parentsInternalising problemsInternalising problemsBetaR2R2ChFChBetaR2R2ChFChStep 10.1017.1**0.0813.5** Child’s gender−0.25**−0.18** Recurrent disease0.20**0.22**Step 20.200.1040.6**0.150.0724.4** Negative life events0.33**0.26**Step 30.480.2722.8**0.190.045.3** Effortful control−0.10– Pleasure sensitivity0.09– Shyness0.31**0.10 Frustration0.15**– Perceptual sensitivity0.12*– Pleasure intensity0.02−0.09 Fear/worry0.25**0.13The dashes indicate that variables were not entered into the model because no significant univariate relationship was found.*p<0.01**p<0.001
Externalising problems Life events accounted for a significant percentage of the explained variance in children’s reports (R2=0.07). The temperament dimensions accounted for a significant increment in children’s (R2Ch=0.22) and parent’s reports (R2Ch=0.07). Effortful control had a significant unique negative effect for both children and parent, and frustration also for children’s reported externalising problems. The interaction terms did not contribute significantly to the problems reported. The variables entered into children’s and parents’ models explained 29 and 7%, respectively, of the variance (Table 5). VIFs in both models were acceptable, ranging between 1.1–1.2.
Table 5Regression analyses examining temperament as a predictor of externalising problems as reported by children and ill parents ChildrenIll parentsExternalising problemsExternalising problemsBetaR2R2ChFChBetaR2R2ChFChStep 10722.6** Negative life events0.26**–Step 2290.2233.7**0.077.9** Effortful control−0.33**−0.21** Frustration0.23**0.06 Fear/worry0.100.10The dash indicates that the variable was not entered into the model because no significant univariate relationship was found.*p<0.01**p<0.001
Discussion and conclusion
The present study is the first to examine the contribution of temperament to prevalence of problems among adolescent children of parents diagnosed with cancer. The findings suggest that temperament predicted internalising and externalising problems, beyond the effects of socio-demographics (child’ gender), illness-related variables (recurrent disease) and number of negative life-events. The most powerful temperament dimensions for internalising problems were shyness and fear/worry (reactive factor). To a lesser extent, frustration and perceptual sensitivity (children’s reports only) heighten the risk for internalising problems. It was argued that shy and anxious children generally have a tendency to withdraw and are hesitant to seek support from their surroundings [22], whereas seeking support might be important, especially in situations in which a parent had cancer. High levels of frustration are related to reduced ability to regulate attention and emotions, whereby children are less able to relax and direct themselves toward matters other than the stressor [23]. Withdrawn behaviour, anxiety and depression might be consequences of these tendencies. Internalising problems reported by the child also increase when they were highly sensitive to things and people around them (perceptual sensitivity). Although, Rothbart and Bates (1998) hypothesised that children with this kind of sensitivity are more vulnerable for internalising and externalising problems, research on these dimensions is lacking [9]. The relationships between shyness, anxiety and frustration and internalising problems found in children reports have also been found in studies among the general population [9, 24–26].
Those who have a temperament characterised by a low level of effortful control (regulative factor) and a high level of frustration are more at risk to develop externalising problems. The relationship between effortful control and externalising problems has been found in other studies as well [9, 11, 26–29]. Children who have more control show more initiative in undertaking activities, have more ability to shift their attention and to focus and are less easily distracted by circumstances [27]. The control that children have over their behaviour in this regard decreases the chance of externalising problems. As mentioned above, frustration decreases emotional regulation and can lead to externalising problems, in addition to manifesting itself in internalising problems [25]. No effect was found for pleasure intensity, which was in contrast to the results of a recent study among children in the general population. That study found that children who scored high on pleasure intensity experienced more externalising problems [26].
Whether the relationship between temperament and problems is specific for situations in which children are exposed to a parent with cancer, is not clear. It is argued that reactive and regulative temperament factors really are important when the child experienced stressful events [30]. A stressful event, such as illness in the parent brings forth negative emotions, specifically in children high in emotionality. Children with low levels of effortful control may have difficulties to deal with these emotions, and may develop as a consequence emotional or behavioural problems [30].
Although the sons and daughters differed in the problems experienced and in their temperament, the relationship between temperament and problems were similar for both genders, as was found in a previous study [26].
Similar patterns were found for parent’s and self reported problems and temperament in the current study, according to a previous study [26]. Our results showed, however, a stronger relationship between temperament and the problems reported by children than parents’ reports of problems, whilst Oldehinkel and colleagues found the opposite. This inconsistency may be attributed to the informant; Oldehinkel and colleagues used parents’ reports to examine temperament, whereas the current study used self-reports from children. A number of studies found that parents and children perceived the level of problems of children under these circumstances different [19, 31, 48]. The differences in perception may have caused also the differences between parents and children found in the present study.
Another interesting result from this study is that, four of the seven temperament dimensions were related to the number of negative life events experienced. This is consistent with Rothbert’s theory, which suggests that, despite its biological base, temperament is influenced by experiences. The influence of stressful environmental factors on the development of temperament is an interesting phenomenon. Because the current study uses a cross-sectional design, no causal statements can be derived from the results.
The current study is one of few to use self-reports of temperament from a large number of children to examine the relationship between temperament and internalising and externalising problems. It is generally assumed that self-description is an important source of information in the field of personality research. Nonetheless, it was faced with some difficulties. First, Rothbart and colleagues paid a lot of attention to the development of the theory around temperament. The empirical implementation of the EATQ-R, however, was limited. The original structure of the EATQ-R seemed not applicable for the Dutch children in the current study. A recent study among Dutch adolescents reported also some problems with the self-reported version of the EATQ-R [26] and used, therefore, the parent version. Furthermore, the comparison of results from the current study with those of studies that used other theories of temperament was difficult, due to differences in conceptualizing and in labelling with regard to corresponding dimensions. More research using reliable, validated instruments to investigate the temperament of children is necessary. Second, some of the items used to measure temperament resemble items on the problem scale and can cause item-overlap. Previous studies show that the association between constructs remains essentially the same when correcting for possible overlapping items [26, 32, 33] indicating that they are separate concepts. To examine whether patterns were similar among different informants, parent’s reports were used also. Fourth, the current study is cross-sectional. Longitudinal designs may provide more insight into the causality of relationships. Fifth, most of the relations between temperament and problems found in the current study were similar to those found in other studies. The use of a control group may have provided more detailed information about whether the relationships between temperament and problems differed for children of parents with cancer and those of children in the general population. Finally, the current study did not pay attention to the interaction between children’s temperament and other potential predictors, such as parenting, the parent–child relationship and coping. Thomas and Chess introduced the “goodness-of-fit” concept, which means that problems in children arise only when temperament and the expectations of the surroundings are not well adjusted to each other [34].
The fact that temperament of children can have an impact on the prevention of problems by children is important information. Health care providers can use this knowledge to assist parents to take the individual characteristics of the child into account and by means of this to understand their children’s behaviour better. Additionally, parents might be supported to improve the ‘fit’ between the temperament of the child and the consequences of having a parent with cancer.