Abstract Peroxisome proliferator–activated receptor γ coactivator 1-α (PGC-1α) is a highly conserved transcriptional coactivator enriched in metabolically active tissues including liver, adipose, pancreas, and muscle. It plays a role in regulating whole body energy metabolism and its deregulation has been implicated in type 2 diabetes (T2D). A single nucleotide variant of the PPARGC1A gene (rs8192678) is associated with T2D susceptibility, relative risk of obesity and insulin resistance, and lower indices of β cell function. This common polymorphism is within a highly conserved region of the bioactive protein and leads to a single amino acid substitution (glycine 482 to serine). Its prevalence and effects on metabolic parameters appear to vary depending on factors including ethnicity and sex, suggesting important interactions between genetics and cultural/environmental factors and associated disease risk. Interestingly, carriers of the serine allele respond better to some T2D interventions, illustrating the importance of understanding functional impacts of genetic variance on PGC-1α when targeting this pathway for personalized medicine. This review summarizes a growing body of literature surrounding possible links between the PGC-1α Gly482Ser single nucleotide polymorphism and diabetes, with focus on key clinical findings, affected metabolic systems, potential molecular mechanisms, and the influence of geographical or ethnic background on associated risk. Metabolic diseases (including, but not limited to, cardiovascular disease, fatty liver disease, and diabetes) have long been associated with poor diet, age, and a sedentary life. Although these lifestyle factors contribute significantly to disease susceptibility and development of the metabolic syndrome, the influence of genetics is widely accepted. In type 2 diabetes (T2D), there is substantial evidence supporting the role of genetic factors in the susceptibility and pathogenesis of the disease as well as individual response to treatment. For example, there is a 40% chance of diabetes in offspring when one parent has T2D and a 70% concordance of incidence among monozygotic twins (1) compared with 10% between dizygotic twins (2). Although these associations provide convincing evidence for our genome determining disease risk, efforts to identify the specific genes involved have proven challenging. One approach to identify common gene variants associated with disease risk is to perform genome-wide association studies (GWAS) involving large-scale statistical analysis of single nucleotide polymorphism (SNP) enrichment in patient populations. However, for T2D, there remains a lack of agreement and certainty surrounding genetic risk markers for the disease, with <10% of heritability currently explained by GWAS (3, 4). To date, there are at least 80 gene variants associated with diabetes risk (5). These discoveries significantly advanced our knowledge of genetic factors and molecular pathways influencing diabetes risk, yet major roadblocks exist in our ability to translate this knowledge from bench to bedside. Many of the known or predicted target pathways are linked to pancreatic β cell biology or insulin sensitivity, yet functional consequences of most variants have yet to be elucidated. Even in cases where the predicted gene target is clear, the basic biology of many encoded proteins is still unknown. SNP often lie in uncharacterized genomic regions or have unknown/unpredictable effects on protein function. Each SNP in isolation has arguably minor effects on risk and proposed “genetic risk signatures” remain difficult to prove experimentally and do not appear to increase risk predictability better than traditional methods (5). Complementary methodology involving fundamental research, clinical studies, and/or linkage analysis can also be useful to identify disease-associated genes. One target shown to be associated with diabetes using these alternative approaches is the gene encoding peroxisome proliferator–activated receptor γ coactivator 1-α (PGC-1α), PPARGC1A. Is There a Role for Genetic Variation of PPARGC1A in Metabolic Disease? In 1998, an autosomal genomic scan of 363 nondiabetic Pima Indians was performed to uncover genetic loci linked to prediabetic traits (6), identifying locus 4p15-q12 as associated with increased fasting insulin. This domain of human chromosome 4 contains approximately 44 known genes, including clock circadian regulator (CLOCK), huntingtin (HTT), Wolfram syndrome 1 (WFS1), and peroxisome proliferator–activated receptor gamma, coactivator 1 α (PPARGC1A). Deeper analysis of a subset of genes involved in energy metabolism revealed that a common polymorphism within the coding region of PPARGC1A (rs8192678, 1444G>A, Gly482Ser) is associated with acute measures of insulin secretion (7). This SNP encodes a single nucleotide difference in the messenger RNA (mRNA) sequence, resulting in either a serine or a glycine amino acid residue being encoded at the 482 position of human peroxisome proliferator–activated receptor (PPAR) gamma coactivator 1-α (PGC-1α). Around the same time, the PGC-1α protein was identified in a yeast two-hybrid screen searching for PPARγ-interacting factors in brown adipose tissue (8). PGC-1α is a large, unstructured protein with activation and transcription factor binding domains largely concentrated in its N-terminal region (9). This transcriptional coactivator is expressed in various metabolically active tissues, including muscle, brain, adipose tissue, liver, and heart (10). In addition to PPARγ, PGC-1α enhances activity of other PPARs (11, 12) and associates with various transcription factors required for oxidative metabolism, mitochondrial biogenesis, ROS homeostasis, insulin and glucagon signaling (10). For example, in mouse muscle, PGC-1α is required to maintain expression of mitochondrial proteins required for oxidative phosphorylation and its loss leads to disrupted glucose metabolism and exercise capacity (13). In the β cell and adipose tissue, PGC-1α is mostly dispensable for maintenance of mitochondrial genes involved in oxidative capacity, but significantly affects insulin secretion and insulin sensitivity, respectively (14–16). These examples highlight the highly tissue-specific nature of PGC-1α function and its considerable involvement in whole-body energy metabolism. In the years following, the importance of PGC-1α in controlling multiple aspects of mitochondrial structure and biology became well established, often leading to the protein being labeled a master regulator of mitochondrial function (17). Soon after, a second clinical study investigating the influence of seven PGC-1α genetic variants in Danish Caucasians independently identified associations between T2D incidence and the PGC-1α Gly482Ser polymorphism (18). With growing knowledge of its biological importance in adaptive mitochondrial metabolism, the early clinical evidence linking this PGC-1α SNP to diabetes aligned well with the then emerging hypothesis that mitochondrial dysfunction could underlie diabetes pathology (19). Evidence Linking the PGC-1α G482S Polymorphism to Diabetes Since the discoveries linking PGC-1α to both mitochondrial function and diabetes risk, interest in this protein and the effects of variation within its gene on metabolic health have grown. Many studies have since investigated associations between the PGC-1α 482 risk allele, T2D incidence and related disease traits. Danish Caucasians subjects with a PGC-1α 482Ser allele have significantly higher incidence of T2D compared with those with only 482Gly alleles, corresponding to a 1.34 relative risk of disease (18). Although a second set of analyses failed to reproduce the association of the Gly482Ser SNP with T2D in Pima Indians, nondiabetic Pima Indians carrying one or two serine-containing alleles have higher insulin secretion 3 and 30 minutes following glucose infusion with equal blood glucose levels (7), suggesting increased insulin resistance. 482Ser carriers also have lower free fatty acids, smaller adipocyte size, and higher rates of lipid oxidation even in the presence of hyperinsulinemia (7), suggesting that the serine-containing allele reduces insulin effectiveness in a potentially dominant manner. Consistently, obese Caucasians of Italian descent with a 482Ser allele have decreased insulin sensitivity [by homeostatic model assessment of insulin resistance (HOMA-IR)] and increased fasting insulin independent of age, sex, body mass index (BMI), high-density lipoprotein-cholesterol or triglycerides, and regardless of heterozygosity or homozygosity at the gene locus (20). Japanese subjects with one or two 482Ser-containing alleles also present with increased fasting insulin and insulin resistance following adjustment for BMI, age, and sex in a population of >900 subjects (21). Despite a growing number of studies showing association with disease traits, incidence of the serine-encoding allele is not consistently elevated in all subjects with diabetes (22, 23), pointing to the importance of other genetic or environmental factors influencing associated risk. Haplotypes containing the 482Ser variant and other PGC-1α polymorphisms (such as the synonymous Thr394Thr PGC-1α variant) are also significantly associated with T2D incidence (21, 24) and impaired oral glucose tolerance in offspring of subjects with T2D (25), suggesting an additive or modifying role for the Gly482Ser polymorphism on other diabetes risk alleles. The Gly482Ser PGC-1α SNP interacts with the widely known Pro12Ala PPARγ diabetes-associated risk allele (26) to influence fasting and postprandial insulin, as well as HOMA-IR (27). With PPARγ as an established downstream target of PGC-1α, identification of two genetic variants converging on one signaling pathways is compelling evidence for the role of altered PPAR function in diabetes pathogenesis. Accordingly, PPARγ agonists (e.g., TZDs) remain some of the most highly effective insulin-sensitizing drugs, albeit their use is now limited because of side effects that include weight gain, edema, loss of bone density, increased cardiovascular risk, and possible bladder cancer (28–30). Other haplotypes containing the Gly482Ser of PGC-1α and SNPs of PPARδ, UCP1, or UCP2 confer a greater risk of diabetes or reduced responses to treatment (31–36). Table 1 summarizes interactions between the PGC-1α Gly482Ser variants and other polymorphisms influencing metabolic disease. Although these observations suggest that the PGC-1α Gly482Ser SNP has a more modifying than causative role in diabetes pathogenesis, when combined with additional risk factors such as obesity, diet, and low exercise, genetic variation of this locus appears to have important implications on overall risk assessment, disease severity, and treatment strategy. Consistent with this, a study of 3244 participants aged 20 to 59 (Netherlands) shows that carriers of the 482Ser allele with BMI < 25 kg/m2 have lower blood glucose (40), whereas the relationship becomes inversed when BMI reaches >25 kg/m2 and carriers of the 482Ser allele instead present with higher fasting glucose. Because overall metabolic health involves a combination of factors and organs systems, the tissue-specific function(s) of PGC-1α may be important in determining mechanisms linking this SNP to diabetes. Table 1. Interactions Between PGC-1α Variants and Other Polymorphisms That Influence Metabolic Disease Pathogenesis or Treatment Polymorphism (Gene) Independent Effect Interaction With rs8192678 (PGC-1α G482S) References rs6902123 (PPARδ) T2D risk (2.7-fold); reduced response to lifestyle intervention on adiposity, hepatic fat storage, muscle mass Together increase risk of conversion from IGT to T2D (additive) (31, 37) rs3734254 (PPARδ) Unknown Together, part of haplotype that associates with 2.5-fold higher risk of T2D (additive) (31) rs2267668 (PPARδ) Reduced improvement in aerobic capacity and insulin sensitivity during exercise Additive effects of SNPs on fitness and insulin sensitivity (35) rs1801282 (PPARγ) Increased fasting insulin, HOMA-IR, and 2-h glucose PGC1α482Gly/PPARγA12A carriers have higher fasting insulin, HOMA-IR, and insulin AUC (27, 32) rs659366 (UCP2) Minor allele is protective against diabetes risk Interaction increased T2D risk (major UCP2 allele with 482Ser) and protection against T2D (minor allele with 482Gly) (36) rs18000592 (UCP1) G allele associated with impaired fasting glucose or T2D Interaction increased T2D risk (G allele with 482Ser; OR, 1.75) and protection against T2D (A allele with 482Gly; OR, 0.239) (34) rs738409 (PNPLA3) Increased risk of NAFLD and NASH Additive effects of SNPs on NASH risk in obese children (38, 39) Polymorphism (Gene) Independent Effect Interaction With rs8192678 (PGC-1α G482S) References rs6902123 (PPARδ) T2D risk (2.7-fold); reduced response to lifestyle intervention on adiposity, hepatic fat storage, muscle mass Together increase risk of conversion from IGT to T2D (additive) (31, 37) rs3734254 (PPARδ) Unknown Together, part of haplotype that associates with 2.5-fold higher risk of T2D (additive) (31) rs2267668 (PPARδ) Reduced improvement in aerobic capacity and insulin sensitivity during exercise Additive effects of SNPs on fitness and insulin sensitivity (35) rs1801282 (PPARγ) Increased fasting insulin, HOMA-IR, and 2-h glucose PGC1α482Gly/PPARγA12A carriers have higher fasting insulin, HOMA-IR, and insulin AUC (27, 32) rs659366 (UCP2) Minor allele is protective against diabetes risk Interaction increased T2D risk (major UCP2 allele with 482Ser) and protection against T2D (minor allele with 482Gly) (36) rs18000592 (UCP1) G allele associated with impaired fasting glucose or T2D Interaction increased T2D risk (G allele with 482Ser; OR, 1.75) and protection against T2D (A allele with 482Gly; OR, 0.239) (34) rs738409 (PNPLA3) Increased risk of NAFLD and NASH Additive effects of SNPs on NASH risk in obese children (38, 39) Phenotypes associated with genetic haplotypes containing the PPARGC1A rs8192678 SNP and other SNPs. Abbreviations: AUC, area under the curve; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; OR, odds ratio. View Large How Does the Gly482Ser PGC-1α Polymorphism Influence Metabolic Disease? Adiposity and BMI While investigating effects of metformin and lifestyle intervention on T2D in patients with high fasting glucose and impaired glucose tolerance, researchers found that the Gly482Ser PGC-1α polymorphism independently associated with increased adiposity (41). 482Ser allele carriers have elevated baseline HOMA-IR and subcutaneous adiposity, but this association is no longer statistically significant when adjusted for BMI (41). The serine allele is also associated with elevated body fat mass in Korean children (42) and in overweight, nondiabetic Chinese adults (43). Moreover, an increase in total body fat mass, hip circumference, BMI, and body fat ratio is seen in 482Ser/482Ser homozygotes of Mexican-Mestizo descent populations (44) and excessive weight gain associates with the 482Ser allele in males with type 1 diabetes receiving intensive diabetes therapy (45). On the contrary, there is no association between the SNP and obesity in Danish Caucasians (46) or in a population of Asian Indians (47). In Pima Indians, nonesterified fatty acids (NEFAs) level are lower in 482Ser carriers (7), suggesting that adipose tissue lipolysis may be impaired. Obese Caucasian carriers of the 482Ser allele have reduced clearance of NEFA following oral glucose challenge (48). Elevated postprandial NEFA can inhibit insulin signaling and glucose disposal and promote lipoprotein secretion from liver, which all contribute to T2D pathogenesis. Taken together, available data suggest that the Gly482Ser PGC-1α polymorphism negatively affects adipose tissue biology or function, and, specifically, the serine-containing allele of PGC-1α confers a higher risk of obesity in certain populations. Thus, links between this SNP and increased T2D susceptibility may simply be due to effects on adiposity, which could explain why carriers of the 482Ser allele benefit more from interventions aimed at weight loss, including caloric restriction (49), bariatric surgery (50), and acarbose treatment (32). Serine-allele carriers are also less responsive to the beneficial effects of rosiglitazone on postprandial insulin, high-density lipoprotein, blood glucose (fasting and postprandial), and HOMA-IR (51). Insulin sensitivity Multiple studies show a substantial correlation between the 482Ser and indices of insulin resistance (usually HOMA-IR) in humans (20, 21, 41, 42, 49). Insulin resistance, typically measured by the effectiveness of insulin to decrease blood glucose, is a complex pathology involving multiple tissue types and mechanisms. The role of PGC-1α in regulating the action and sensitivity of insulin in tissues is still unclear. PPARGC1A mRNA is decreased in muscle and adipose tissue of human subjects with diabetes (52–54), and there appears to be a correlation between adipose PGC-1α protein levels and decreased insulin sensitivity (53). Consistently, 482Ser allele carriers appear to have decreased PPARGC1A mRNA compared with Gly/Gly subjects (14), although it is unclear how this happens. PGC-1α null mice fail to become obese on a high-fat diet, likely because of defects in the central nervous system (55, 56). However, tissue-specific reduction of PGC-1α expression in adipose, liver, and muscle (with aging) reduce glucose tolerance and insulin sensitivity in mice (13, 16, 57, 58), supporting the hypothesis that decreased PGC-1α expression in insulin-sensitive tissues may contribute to higher risk for T2D. Insulin secretion PGC-1α levels in the endocrine pancreas appear to directly regulate β cell function. Pancreatic islets from subjects with T2D have significantly lower PGC-1α expression and nondiabetic donor islets with a 482Ser allele (Ser/Ser or Gly/Ser) have reduced capacity to secrete insulin ex vivo in response to glucose compared with those with two 482Gly alleles (14). In contrast, β cell function by HOMA-%B (an indirect index based on concentrations of circulating insulin and glucose) is increased in nondiabetic serine allele carriers (7, 20), although it is not clear whether this reflects a primary effect on insulin secretion or is secondary to compensate for peripheral insulin resistance. High levels of PGC-1α are detected in islets of Zucker diabetic fatty rats and ob/ob mice, two animal models of T2D (59). However, overexpression of PGC-1α in mouse islets within physiological levels in vivo does not impair β cell function in adult mice (60). β cell–specific knockout of both PGC-1α and related PGC-1β diminishes glucose stimulated insulin secretion (15), which is consistent with data in humans (14) and implies that PGC-1α expression in the pancreatic islets directly correlates with insulin secretion (61). Despite effects on glucose-stimulated insulin secretion, decreased PGC-1α in β cells does not affect whole body glucose tolerance in lean or obese mice (15). Therefore, low PGC-1α in β cells may not play a predominant role in increased diabetes risk associated with gene variation, but may be additive or synergistic with effects on adiposity and insulin sensitivity in other tissues. Cardiac and liver metabolism In addition to T2D, the Gly482Ser polymorphism is associated with both cardiovascular disease and nonalcoholic fatty liver disease (NAFLD). There is increased risk for hypertrophic cardiomyopathy [odds ratio (OR), 1.11 to 2.11] in carriers of the 482Ser allele (62). Consistently, Mongolian carriers of the 482Ser allele have higher incidences of hypertension (63) and hypertensive patients homozygous for the serine allele have greater left ventricular hypertrophy and lower diastolic function (64). There is also a higher incidence of the 482Ser allele among patients of Chinese descent with coronary artery disease (OR, 1.53) (65). DNA damage (measured by 8-OHdG levels in urine) is significantly higher in Puerto Rican carriers of the serine risk allele (66). Urinary 8-OHdG is a biomarker of general oxidative stress and is increased in hypertensive patients, but may also serve as a biomarker of cancer, atherosclerosis, and diabetes, correlating well with severity of diabetic nephropathy and retinopathy. In addition to direct effects on heart function, homozygosity of the PGC-1α 482Ser allele is also associated with increased circulating small, dense low-density lipoprotein particles, which are considered a predictor of coronary artery disease (67). Fatty liver disease increases the risk of developing T2D by two- to threefold (68). With recent interest in metabolic liver disease as a predisposing factor to diabetes, NAFLD risk genes may also be useful in predicting diabetes risk. The PGC-1α Gly482Ser polymorphism increases risk of NAFLD in obese Taiwanese children (OR, 1.74) compared with controls homozygous for the glycine variant (38). In obese Taiwanese adults, one serine allele is an independent risk factor for nonalcoholic steatohepatitis (NASH; a disease associated with higher steatosis and ballooning degeneration of liver cells) and carries an additive effect on NASH incidence when combined with the PNPLA3 rs738409 SNP (39). In contrast, newly diagnosed German subjects with diabetes homozygous for the 482Gly allele have lower hepatic adenosine triphosphate, which could suggest impaired mitochondrial metabolism (69). Microvascular disease If diabetes is undiagnosed or mismanaged, it can lead to several secondary complications, including eye, kidney, and nerve damage. Intriguingly, the Gly482Ser PGC-1α polymorphism may also play a role in the development of these comorbidities. In a Slovene (Caucasian) population with T2D, there is a higher frequency of the serine allele (OR, 2.7) among subjects with diabetic retinopathy (70). The serine allele is also associated with diabetic nephropathy in Asian Indians (OR, 2.14) (71) and Koreans with T2D (72), and may increase risk for micro/macroalbuminuria by 70% (67). Conversely, in a Caucasian cohort from the United Kingdom, nondiabetic carriers of the serine allele appear protected against new-onset diabetes following kidney transplant (OR, 0.26) (73) and the serine allele protects against T2D in subjects of Chinese descent with high levels of serum uric acid, an independent risk factor for diabetes (74, 75). Variables Affecting Strength of the rs8192678-Diabetes Association Despite mounting clinical evidence and association studies, the PGC-1α Gly482Ser polymorphism has yet to be identified in GWAS for diabetes. Although largely unbiased in their design, GWAS studies have limitations. It is widely accepted that diabetes risk is influenced by ethnicity; however, GWAS for this disease are mostly limited to Caucasian populations of European descent or Asian populations of Japanese or Chinese descent (5). More recent efforts incorporating multiethnic analyses have confirmed many of the previously identified genes and identified candidates that appear specific for certain genetic backgrounds (22). Unfortunately, the goal to identify genes influencing worldwide diabetes risk often leads to variants found only in specific populations being ignored or undervalued. The influence of sex on diabetes risk is also well established in the clinic, with males being more susceptible than women (before menopause); however, for the most part, males and females are grouped together in these large-scale genomic analyses. Finally, we are only beginning to appreciate the possible implications of geographical, cultural, environmental, or even sociological influence on the genetic basis of the disease, and these are rarely considered in GWAS. In the age of personalized medicine, understanding the biological implications of identified gene variants and how they are influenced by environment may prove more valuable than generalized lists of “risk” vs “protective” genes. The influence of sex on PGC-1α and the Gly482Ser polymorphism Accumulating evidence suggests PGC-1α expression and activity may be significantly influenced by sex. The 482Ser allele is associated with reduced risk of diastolic dysfunction (OR, 0.13 to 0.19) in Swedish men (76), but no effect is observed in women. In contrast, a substantial increase in arterial hypertension prevalence is reported only for male French Caucasians with a 482Ser allele (77). In Japanese males with T2D, having one or two 482Ser alleles lowers circulating adiponectin (an antidiabetic adipokine), whereas PPARGC1A SNP genotype has no influence on adiponectin concentrations in females. In contrast, only 482Ser/482Ser Japanese women trend toward having higher fasting plasma glucose (78). Although seemingly contradictory results are presented (protective vs detrimental effects of variant; varying influences of sex), it is interesting to note that that the French and Japanese subjects also had T2D, again suggesting that additional lifestyle and genetic factors play a modifying role in PGC-1α activity. In support of this, sedentary Swedish males carrying a 482Ser allele have a greater risk of developing obesity with age; but again, no association with obesity, age, or activity is reported for Swedish females (79). Thus, there is compelling evidence that sex has an important effect on the outcome of this SNP on metabolic disease, often having more substantial outcomes in one sex over another and influenced by environmental factors such as activity, diet, and age. Although clear metabolic differences in clinical studies suggest that sex is an important factor influencing PGC-1α activity, very few studies attempt to explain the sexually dimorphic metabolic phenotypes. A possible explanation comes from recent evidence in mouse liver and brain suggesting that PGC-1α expression is regulated by sex hormone signaling, and that the coactivator directly interacts with sex hormone receptors to augment their activity. In the brain, female mice have lower markers of hypothalamic inflammation in response to a high-fat diet. It was found that this phenomenon could be linked to the anti-inflammatory properties of PGC-1α and ERα in the female brain (80). Our laboratory demonstrated that PGC-1α is particularly important for ROS detoxification and protection from NASH in female mice. In the presence of estrogen, PGC-1α coactivates ERα to enhance hepatic mitochondrial ROS defenses in response to diets high in fat and fructose (57). In addition, estrogen promotes PGC-1α expression in hepatocytes in a PGC-1α–dependent manner, feeding forward to amplify ROS-detoxifying pathways. This feed forward mechanism involving estrogen is consistent with female mice having three- to fourfold higher levels of the coactivator in liver than male littermates. Given the importance of PGC-1α activity on estrogen’s protective effects in liver, female mice are much more susceptible to obesity-induced oxidative damage when PGC-1α activity is reduced (57). Selective pressure and ethnicity The PGC-1α protein, and the domain containing the Gly482Ser SNP in particular, are highly conserved across species (81). Interestingly, although the serine containing allele is arguably the “minor allele” in many human populations (81), most other vertebrates have a serine in this position. Searching available databases, we and others (81) have found only humans (Homo sapiens), chickens (Gallus gallus), and wild turkeys (Meleagris gallopavo) to have a glycine-containing allele; however, many species of birds (e.g., Columba livia, or rock pigeon) and the fruit bat (Rousettus aegyptiacus) alternatively have an arginine (R) residue. These limited data suggest the glycine-containing variant may have appeared later in evolution and became enriched in humans from selective pressure, or it was selected against in other species. A higher than expected prevalence of 482Gly/482Gly genotype might also suggest that this allele provides selective advantage to certain groups of people. However, if the 482Ser variant is truly a strong risk allele for metabolic disease in humans, why does this variant remain so prevalent in humans, and why is it not selected against in other species? A plausible explanation might be that the onset of metabolic disease often occurs years after reproductive age, providing no resistance to the allele being passed to future progeny. However, if the SNP has no influence before disease onset, one would expect classical Mendelian ratios of SNP prevalence, which is not the case in most populations tested. The majority of published clinical studies on this SNP were performed in subjects of European descent, where the prevalence of the 482Gly/482Gly phenotype averages around 50%, with 482Ser/482Ser homozygotes detected at rates of 10% to 15% (82). Interestingly, the prevalence of each variant seems to vary greatly depending on geographical location (83). Sampling data suggest that 482Ser “risk” allele prevalence can reach >80% in some Polynesian island nations of the South Pacific, whereas many areas within Africa, Papua New Guinea, and Indonesia have frequencies <3% (based on data from the Human Genome Diversity Cell Line Panel) (83). These large variant frequency differences between populations led to a theory that the 482Ser allele may be considered a “thrifty gene,” providing advantages to species who depend on fat storage capacity for survival (e.g., during periods of famine in both humans and rodents or hibernation in rodents) (83). However, in times of relative food abundance, having the serine variant may promote obesity and increase metabolic disease prevalence. Recently, this hypothesis was challenged, as statistical testing did not find evidence for departure from natural evolution for this locus in a range of Polynesian, Asian, European, or African populations (84). Additional evidence against the thrifty gene hypothesis comes from the fact that relative risk of T2D associated with the 482Ser allele is not the same across populations of humans that are now exposed to relatively similar diets and lifestyles. This brings into question whether ethnicity plays a role in disease risk through interactions between this SNP and other disease-modifying genes or environmental factors. In Caucasian populations the risk of T2D is only modestly increased by the PGC-1α Gly482Ser SNP (i.e., OR, 1.1 to 1.8), whereas OR for T2D risk increase greatly in 482Ser carrying subjects of Northern Indian (OR, 2.04 to 3.19), Iranian (OR, 9.0), Chinese (OR, 1.64 to 1.85), and Tunisian descent (OR 1.17 to 2.98) (36, 85–88). Thus, it is plausible that differences in disease risk between ethnicities linked to this polymorphism are due to additive or synergistic effects with other genetic modifiers or environmental factors specific to geographical region. Table 2 summarizes clinical data concerning effect(s) of the rs8192678 SNP and T2D risk or other risk factors of metabolic disease, including ethnicity. Table 2. Risk of Diabetes Associated With the Gly482Ser Polymorphism Varies With Ethnicity Nationality/Ethnicity 482Ser Association Association With T2D and/or its Risk Factors (Y/N) Cohort Size (M/F) References Meta-analysis (Caucasian, East Asian, Indian) T2D risk (pooled OR: 1.19) Y 17,101 (89) Diabetes prevention program Elevated insulin resistance Y 3234 (41) African American, Hispanic, Asian America, American Indian French Canadian Lower fasting insulin levels, and 2-hr glucose levels N 680 (291/389) (27) French Canadian No association with T2D risk N 276 (79/197) (90) Hispanic and non-Hispanic Caucasian (Colorado) No association with T2D risk N 3090 (91) STOP-NIDDM T2D risk (1.5-fold) Y 769 (31) Pima Indian 482Gly homozygotes have lower mean insulin secretory response; no association with T2D risk N 1185 (201/984) (7) Danish Caucasian T2D risk (1.34 relative risk) Y 1146 (18) Danish No association with metabolic syndrome N 2349 (46) Canadian, German, Austrian, Finnish, Swedish, Dane Risk of conversion from impaired glucose tolerance to T2D (1.6-fold) Y 770 (387/383) (32) Finnish Higher OGTT in children of T2D parents Y 156 (25) Slovene (Caucasian) T2D risk (1.9-fold) Y 545 (82) Italian Insulin resistance in obese subjects Y 348 (103/245) (20) Spanish Higher HOMA-IR and insulin concentrations Y 180 (49) Dutch Reduced blood glucose in nonobese; higher blood glucose in obese Y 3244 (1552/1692) (40) Tunisian Associated with T2D (OR, 1.35) Y 889 (88) North Indian (Kashmir, Punjab, and Jammu) T2D risk (2.04-fold) Y 822 (479/343) (85) North Indian (Kashmir, Punjab, and Jammu) 482Gly homozygotes associated with protection against T2D (>fourfold) Y 1686 (834/852) (36) South Indian (Chennai) No association with T2D N 1397 (92) Indian No association with T2D N 164 (47) Kurdish Iranian T2D risk (OR, 5.23) Y 346 (155/191) (86) Chinese Han T2D risk (OR, 1.54) Y 1505 (93) Chinese Han No association with T2D N 651 (63) Chinese Han No association with T2D N 2301 (74) Chinese Han No association with T2D N 1090 (94) Southern Chinese T2D risk, increased cholesterol and LDL Y 545 (95) Northern Chinese T2D risk (1.645-fold) Y 915 (475/440) (87) Chinese (Shanghai – Han) T2D risk (OR, 1.85) Y 577 (96) Korean Increased body fat, insulin resistance, and fasting insulin Y 286 (42) Japanese Higher fasting insulin and insulin resistance Y 537 (277/260) (21) Japanese No association with BMI, fasting glucose, or insulin levels N 155 (78/81) (78) Nationality/Ethnicity 482Ser Association Association With T2D and/or its Risk Factors (Y/N) Cohort Size (M/F) References Meta-analysis (Caucasian, East Asian, Indian) T2D risk (pooled OR: 1.19) Y 17,101 (89) Diabetes prevention program Elevated insulin resistance Y 3234 (41) African American, Hispanic, Asian America, American Indian French Canadian Lower fasting insulin levels, and 2-hr glucose levels N 680 (291/389) (27) French Canadian No association with T2D risk N 276 (79/197) (90) Hispanic and non-Hispanic Caucasian (Colorado) No association with T2D risk N 3090 (91) STOP-NIDDM T2D risk (1.5-fold) Y 769 (31) Pima Indian 482Gly homozygotes have lower mean insulin secretory response; no association with T2D risk N 1185 (201/984) (7) Danish Caucasian T2D risk (1.34 relative risk) Y 1146 (18) Danish No association with metabolic syndrome N 2349 (46) Canadian, German, Austrian, Finnish, Swedish, Dane Risk of conversion from impaired glucose tolerance to T2D (1.6-fold) Y 770 (387/383) (32) Finnish Higher OGTT in children of T2D parents Y 156 (25) Slovene (Caucasian) T2D risk (1.9-fold) Y 545 (82) Italian Insulin resistance in obese subjects Y 348 (103/245) (20) Spanish Higher HOMA-IR and insulin concentrations Y 180 (49) Dutch Reduced blood glucose in nonobese; higher blood glucose in obese Y 3244 (1552/1692) (40) Tunisian Associated with T2D (OR, 1.35) Y 889 (88) North Indian (Kashmir, Punjab, and Jammu) T2D risk (2.04-fold) Y 822 (479/343) (85) North Indian (Kashmir, Punjab, and Jammu) 482Gly homozygotes associated with protection against T2D (>fourfold) Y 1686 (834/852) (36) South Indian (Chennai) No association with T2D N 1397 (92) Indian No association with T2D N 164 (47) Kurdish Iranian T2D risk (OR, 5.23) Y 346 (155/191) (86) Chinese Han T2D risk (OR, 1.54) Y 1505 (93) Chinese Han No association with T2D N 651 (63) Chinese Han No association with T2D N 2301 (74) Chinese Han No association with T2D N 1090 (94) Southern Chinese T2D risk, increased cholesterol and LDL Y 545 (95) Northern Chinese T2D risk (1.645-fold) Y 915 (475/440) (87) Chinese (Shanghai – Han) T2D risk (OR, 1.85) Y 577 (96) Korean Increased body fat, insulin resistance, and fasting insulin Y 286 (42) Japanese Higher fasting insulin and insulin resistance Y 537 (277/260) (21) Japanese No association with BMI, fasting glucose, or insulin levels N 155 (78/81) (78) Summary of clinical studies evaluating associations between the rs8192678 SNP of PPARGC1A and T2D risk or other risk factors of metabolic disease. Ethnicity and sex of participants, specific metabolic parameters assessed, OR, and whether a substantial association with T2D was revealed (Y/N) are included when available. Abbreviations: F, female; M, male; N, no; OGTT, oral glucose tolerance test; OR, odds ratio; STOP-NIDDM, Study to Prevent Non-Insulin-Dependent Diabetes Mellitus; Y, yes. View Large How Does the Polymorphism Affect PGC-1α Structure and Function? Now, almost 20 years later, the functional role of PGC-1α in cells has expanded greatly, yet it arguably remains an essential regulator of mitochondrial function and energy metabolism. PGC-1α transcription, protein stability, and activity are very precisely controlled. PGC-1α expression levels change rapidly in response to physiological stressors or increased energy demand (such as cold, exercise, fasting, and inflammation) (97) and the protein is quickly degraded (98). Influences of external stimuli on PGC-1α activity, mechanisms controlling mRNA transcription and protein stability are also tissue-specific (17, 99–101). PPARGC1A promoter methylation correlating with reduced PGC-1α gene expression is also implicated in T2D (14, 102, 103). For example, in human diabetic islets, there is a twofold increase in DNA methylation compared with islets from patients without diabetes (14). Additionally, PGC-1α promoter methylation is negatively correlated with PPARGC1A gene expression and mitochondrial number in skeletal muscle from T2D subjects (103). Blood glucose levels following glucose challenge in women with gestational diabetes correlate with placental PPARGC1A promoter methylation (104). In mice, maternal obesity linked to high fat and sugars diets is associated with hypermethylation of the Ppargc1a promoter in muscle of offspring, but this can be reversed with exercise (105). Although these studies collectively show that epigenetic regulation of PPARGC1A correlates with reduced PGC-1α expression and diabetes pathogenesis, it remains unclear whether increased promoter methylation is a cause or consequence of obesity and/or T2D development. An additional level of complexity in PGC-1α regulation is added by extensive posttranslational modification. Phosphorylation, acetylation, ubiquitination, and methylation all affect PGC-1α stability and activity (17). For example, phosphorylation by Akt at 570Ser enhances PGC-1α activity (106), whereas Clk2 phosphorylation of serine residues in the SR region (spanning residues 564-635) decreases coactivator function (17, 107). There are also phosphorylation events that target PGC-1α for ubiquitination and proteosomal degradation, thereby affecting protein stability (108). Although several PGC-1α posttranslational modifications are identified, many remain uncharacterized and downstream consequences of many modifications on metabolism are unclear. Lower PGC-1α expression is commonly associated with poor health outcomes (109), and lower PGC-1α mRNA expression is found in muscle (110) and islets (14) of serine allele carriers with T2D. These studies provided evidence suggesting that a serine at position 482 may reduce PGC-1α levels. Yet, despite clear associations of 482Ser with low PGC-1α expression, it is not clear how this occurs at the molecular level or whether the polymorphism also affects coactivator activity as well as protein level. In vitro analyses show no differences in the ability of the 482Gly and 482Ser variants to induce adiponectin promoter activity following ectopic expression in HeLa cells to equal protein levels (78) and equivalent coactivator activity for the variants is also demonstrated on the ACBP-1C promoter in HepG2 cells (111). These two studies suggest that the polymorphism may not have direct effects on the coactivator function of PGC-1α. However, three other studies show that activity of the 482Ser variant is reduced on PEPCK and CPT1α promoters and gene targets regulating ROS detoxification in HepG2 cells (54, 112), and increased compared with the 482Gly variant on the TFAM promoter in HeLa cells (113) (Table 3). Thus, cell environment as well as the gene target may be determining factors for observed differences in variant activity. Table 3. Mechanistic Studies Investigating Functional Impact of the Gly482Ser Polymorphism Gene/Promoter Cell Type Effect References PPRE HeLa 482Gly PGC-1α has reduced coactivator activity (113) Tfam HeLa 482Gly PGC-1α has reduced coactivator activity (113) ACBP-1C HepG2 No difference in coactivator activity between variants (111) Adiponectin HeLa No difference in coactivator activity between variants (78) PEPCK HepG2 482Ser PGC-1α has reduced coactivator activity (112) CPT1 HepG2 482Ser PGC-1α has reduced coactivator activity (112) SOD2 and GPX1 HepG2 482Ser PGC-1α has reduced coactivator activity (57) Gene/Promoter Cell Type Effect References PPRE HeLa 482Gly PGC-1α has reduced coactivator activity (113) Tfam HeLa 482Gly PGC-1α has reduced coactivator activity (113) ACBP-1C HepG2 No difference in coactivator activity between variants (111) Adiponectin HeLa No difference in coactivator activity between variants (78) PEPCK HepG2 482Ser PGC-1α has reduced coactivator activity (112) CPT1 HepG2 482Ser PGC-1α has reduced coactivator activity (112) SOD2 and GPX1 HepG2 482Ser PGC-1α has reduced coactivator activity (57) Summary of in vitro studies assessing functional differences between the two variants of human PGC-1α associated with the rs8192678 SNP in cell lines. The cell lines and promoter/gene target evaluated are listed, alongside any differences in activity observed in each system. View Large Another explanation for lower PGC-1α expression and activity in 482Ser carriers is that the amino acid substitution directs or disrupts protein stability. We have shown that the half-life of the 482Ser variant is shorter than the 482Gly variant in cultured liver cells, corresponding with reduced coactivator activity on target genes involved in ROS detoxification (57). If in fact the polymorphism affects only protein degradation pathways, this would explain why coactivator function is similar for the variants when expressed to equal protein levels. It may also explain why carriers of the 482Ser allele have lower PGC-1α mRNA, because PGC-1α feeds back to increase its own expression (114), and, over the long term, expression of a more unstable variant (482Ser) could lead to chronically lower levels. An interesting finding drawn from clinical data is that risk associated with the 482Ser allele is not dose-dependent; in most cases, the OR is similar whether the carrier has one or two serine-containing alleles. To our knowledge, this phenomenon has not been explored at molecular or mechanistic levels, but implies that the 482Ser may have a dominant role on the associated phenotypes (14, 21, 88). However, because most clinical studies group 482Ser/482Gly and 482Ser/482Ser carriers together (possibly for power/statistical purposes), it is impossible to make conclusions from existing data sets and further experimental testing is needed. Conclusions Taken together, there is evidence to suggest that there is an explicit link between the Gly482Ser polymorphism and metabolic syndrome; however, there is a lack of consensus on why the “protective” allele is not more prevalent, whether other genetic and environmental factors strongly influence associated risk, and how allelic variation affects PGC-1α activity. Presently, many studies support the notion that a 482Ser variant increases the likelihood of insulin resistance, β cell dysfunction, and comorbidities related to T2D, especially in cases where BMI is above normal. Remarkably, carriers of the 482Ser may respond better to T2D treatments and lifestyle interventions, illustrating the importance of understanding the biology of these SNPs to facilitate the development of personalized treatments for T2D and other related diseases. Going forward, researchers should consider ethnicity and sex when determining diabetes risk associated with this SNP, because these variables seems to significantly affect outcomes in both clinical and preclinical models. To bring these clinical findings from bench to bedside, efforts should be focused on understanding the molecular consequences and functional significance of the Gly482Ser PGC-1α polymorphism. Abbreviations: BMI body mass index GWAS genome-wide association studies HOMA-IR homeostatic model assessment of insulin resistance mRNA messenger RNA NAFLD nonalcoholic fatty liver disease NASH nonalcoholic steatohepatitis NEFA nonesterified fatty acid OR odds ratio PGC-1α peroxisome proliferator–activated receptor γ coactivator 1-α PPAR peroxisome proliferator–activated receptor SNP single nucleotide polymorphism T2D type 2 diabetes. Acknowledgments Financial Support: This work was supported by Canadian Institutes of Health Research Grants INM143076 and PJT148771 (to J.L.E.). R.V. and N.P.K. are supported by graduate scholarships from McGill University. Disclosure Summary: The authors have nothing to disclose. References 1. Barnett AH, Eff C, Leslie RD, Pyke DA. Diabetes in identical twins. A study of 200 pairs. Diabetologia . 1981; 20( 2): 87– 93. Google Scholar CrossRef Search ADS PubMed 2. Newman B, Selby JV, King MC, Slemenda C, Fabsitz R, Friedman GD. Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins. Diabetologia . 1987; 30( 10): 763– 768. Google Scholar CrossRef Search ADS PubMed 3. Flannick J, Florez JC. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet . 2016; 17( 9): 535– 549. Google Scholar CrossRef Search ADS PubMed 4. Basile KJ, Johnson ME, Xia Q, Grant SF. Genetic susceptibility to type 2 diabetes and obesity: follow-up of findings from genome-wide association studies. Int J Endocrinol . 2014; 2014: 769671. Google Scholar CrossRef Search ADS PubMed 5. Wang X, Strizich G, Hu Y, Wang T, Kaplan RC, Qi Q. Genetic markers of type 2 diabetes: progress in genome-wide association studies and clinical application for risk prediction. J Diabetes . 2016; 8( 1): 24– 35. Google Scholar CrossRef Search ADS PubMed 6. Pratley RE, Thompson DB, Prochazka M, Baier L, Mott D, Ravussin E, Sakul H, Ehm MG, Burns DK, Foroud T, Garvey WT, Hanson RL, Knowler WC, Bennett PH, Bogardus C. An autosomal genomic scan for loci linked to prediabetic phenotypes in Pima Indians. J Clin Invest . 1998; 101( 8): 1757– 1764. Google Scholar CrossRef Search ADS PubMed 7. Muller YL, Bogardus C, Pedersen O, Baier L. A Gly482Ser missense mutation in the peroxisome proliferator-activated receptor gamma coactivator-1 is associated with altered lipid oxidation and early insulin secretion in Pima Indians. Diabetes . 2003; 52( 3): 895– 898. Google Scholar CrossRef Search ADS PubMed 8. Puigserver P, Wu Z, Park CW, Graves R, Wright M, Spiegelman BM. A cold-inducible coactivator of nuclear receptors linked to adaptive thermogenesis. Cell . 1998; 92( 6): 829– 839. Google Scholar CrossRef Search ADS PubMed 9. Devarakonda S, Gupta K, Chalmers MJ, Hunt JF, Griffin PR, Van Duyne GD, Spiegelman BM. Disorder-to-order transition underlies the structural basis for the assembly of a transcriptionally active PGC-1α/ERRγ complex. Proc Natl Acad Sci USA . 2011; 108( 46): 18678– 18683. Google Scholar CrossRef Search ADS PubMed 10. Finck BN, Kelly DP. PGC-1 coactivators: inducible regulators of energy metabolism in health and disease. J Clin Invest . 2006; 116( 3): 615– 622. Google Scholar CrossRef Search ADS PubMed 11. Kleiner S, Nguyen-Tran V, Baré O, Huang X, Spiegelman B, Wu Z. PPARdelta agonism activates fatty acid oxidation via PGC-1alpha but does not increase mitochondrial gene expression and function. J Biol Chem . 2009; 284( 28): 18624– 18633. Google Scholar CrossRef Search ADS PubMed 12. Vega RB, Huss JM, Kelly DP. The coactivator PGC-1 cooperates with peroxisome proliferator-activated receptor alpha in transcriptional control of nuclear genes encoding mitochondrial fatty acid oxidation enzymes. Mol Cell Biol . 2000; 20( 5): 1868– 1876. Google Scholar CrossRef Search ADS PubMed 13. Sczelecki S, Besse-Patin A, Abboud A, Kleiner S, Laznik-Bogoslavski D, Wrann CD, Ruas JL, Haibe-Kains B, Estall JL. Loss of Pgc-1α expression in aging mouse muscle potentiates glucose intolerance and systemic inflammation. Am J Physiol Endocrinol Metab . 2014; 306( 2): E157– E167. Google Scholar CrossRef Search ADS PubMed 14. Ling C, Del Guerra S, Lupi R, Rönn T, Granhall C, Luthman H, Masiello P, Marchetti P, Groop L, Del Prato S. Epigenetic regulation of PPARGC1A in human type 2 diabetic islets and effect on insulin secretion. Diabetologia . 2008; 51( 4): 615– 622. Google Scholar CrossRef Search ADS PubMed 15. Oropeza D, Jouvet N, Bouyakdan K, Perron G, Ringuette L-J, Philipson LH, Kiss RS, Poitout V, Alquier T, Estall JL. PGC-1 coactivators in β-cells regulate lipid metabolism and are essential for insulin secretion coupled to fatty acids. Mol Metab . 2015; 4( 11): 811– 822. Google Scholar CrossRef Search ADS PubMed 16. Kleiner S, Mepani RJ, Laznik D, Ye L, Jurczak MJ, Jornayvaz FR, Estall JL, Chatterjee Bhowmick D, Shulman GI, Spiegelman BM. Development of insulin resistance in mice lacking PGC-1α in adipose tissues. Proc Natl Acad Sci USA . 2012; 109( 24): 9635– 9640. Google Scholar CrossRef Search ADS PubMed 17. Fernandez-Marcos PJ, Auwerx J. Regulation of PGC-1α, a nodal regulator of mitochondrial biogenesis. Am J Clin Nutr . 2011; 93( 4): 884S–8 90S. Google Scholar CrossRef Search ADS PubMed 18. Ek J, Andersen G, Urhammer SA, Gaede PH, Drivsholm T, Borch-Johnsen K, Hansen T, Pedersen O. Mutation analysis of peroxisome proliferator-activated receptor-γ coactivator-1 (PGC-1) and relationships of identified amino acid polymorphisms to type II diabetes mellitus. Diabetologia . 2001; 44( 12): 2220– 2226. Google Scholar CrossRef Search ADS PubMed 19. Fosslien E. Mitochondrial medicine--molecular pathology of defective oxidative phosphorylation. Ann Clin Lab Sci . 2001; 31( 1): 25– 67. Google Scholar PubMed 20. Fanelli M, Filippi E, Sentinelli F, Romeo S, Fallarino M, Buzzetti R, Leonetti F, Baroni MG. The Gly482Ser missense mutation of the peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1 alpha) gene associates with reduced insulin sensitivity in normal and glucose-intolerant obese subjects. Dis Markers . 2005; 21( 4): 175– 180. Google Scholar CrossRef Search ADS PubMed 21. Hara K, Tobe K, Okada T, Kadowaki H, Akanuma Y, Ito C, Kimura S, Kadowaki T. A genetic variation in the PGC-1 gene could confer insulin resistance and susceptibility to Type II diabetes. Diabetologia . 2002; 45( 5): 740– 743. Google Scholar CrossRef Search ADS PubMed 22. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium; Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium; South Asian Type 2 Diabetes (SAT2D) Consortium; Mexican American Type 2 Diabetes (MAT2D) Consortium; Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in muylti-Ethnic Samples (T2D-GENES) Consortium; Mahajan A, Go MJ, Zhang W, Below JE, Gaulton KJ, Ferreira T, Horikoshi M, Johnson AD, Ng MC, Prokopenko I, Saleheen D, Wang X, Zeggini E, Abecasis GR, Adair LS, Almgren P, Atalay M, Aung T, Baldassarre D, Balkau B, Bao Y, Barnett AH, Barroso I, Basit A, Been LF, Beilby J, Bell GI, Benediktsson R, Bergman RN, Boehm BO, Boerwinkle E, Bonnycastle LL, Burtt N, Cai Q, Campbell H, Carey J, Cauchi S, Caulfield M, Chan JC, Chang LC, Chang TJ, Chang YC, Charpentier G, Chen CH, Chen H, Chen YT, Chia KS, Chidambaram M, Chines PS, Cho NH, Cho YM, Chuang LM, Collins FS, Cornelis MC, Couper DJ, Crenshaw AT, van Dam RM, Danesh J, Das D, de Faire U, Dedoussis G, Deloukas P, Dimas AS, Dina C, Doney AS, Donnelly PJ, Dorkhan M, van Duijn C, Dupuis J, Edkins S, Elliott P, Emilsson V, Erbel R, Eriksson JG, Escobedo J, Esko T, Eury E, Florez JC, Fontanillas P, Forouhi NG, Forsen T, Fox C, Fraser RM, Frayling TM, Froguel P, Frossard P, Gao Y, Gertow K, Gieger C, Gigante B, Grallert H, Grant GB, Grrop LC, Groves CJ, Grundberg E, Guiducci C, Hamsten A, Han BG, Hara K, Hassanali N, Hattersley AT, Hayward C, Hedman AK, Herder C, Hofman A, Holmen OL, Hovingh K, Hreidarsson AB, Hu C, Hu FB, Hui J, Humphries SE, Hunt SE, Hunter DJ, Hveem K, Hydrie ZI, Ikegami H, Illig T, Ingelsson E, Islam M, Isomaa B, Jackson AU, Jafar T, James A, Jia W, Jöckel KH, Jonsson A, Jowett JB, Kadowaki T, Kang HM, Kanoni S, Kao WH, Kathiresan S, Kato N, Katulanda P, Keinanen-Kiukaanniemi KM, Kelly AM, Khan H, Khaw KT, Khor CC, Kim HL, Kim S, Kim YJ, Kinnunen L, Klopp N, Kong A, Korpi-Hyövälti E, Kowlessur S, Kraft P, Kravic J, Kristensen MM, Krithika S, Kumar A, Kumate J, Kuusisto J, Kwak SH, Laakso M, Lagou V, Lakka TA, Langenberg C, Langford C, Lawrence R, Leander K, Lee JM, Lee NR, Li M, Li X, Li Y, Liang J, Liju S, Lim WY, Lind L, Lindgren CM, Lindholm E, Liu CT, Liu JJ, Lobbens S, Long J, Loos RJ, Lu W, Luan J, Lyssenko V, Ma RC, Maeda S, Mägi R, Männisto S, Matthews DR, Meigs JB, Melander O, Metspalu A, Meyer J, Mirza G, Mihailov E, Moebus S, Mohan V, Mohlke KL, Morris AD, Mühleisen TW, Müller-Nurasyid M, Musk B, Nakamura J, Nakashima E, Navarro P, Ng PK, Nica AC, Nilsson PM, Njølstad I, Nöthen MM, Ohnaka K, Ong TH, Owen KR, Palmer CN, Pankow JS, Park KS, Parkin M, Pechlivanis S, Pedersen NL, Peltonen L, Perry JR, Peters A, Pinidiyapathirage JM, Platou CG, Potter S, Price JF, Qi L, Radha V, Rallidis L, Rasheed A, Rathman W, Rauramaa R, Raychaudhuri S, Rayner NW, Rees SD, Rehnberg E, Ripatti S, Robertson N, Roden M, Rossin EJ, Rudan I, Rybin D, Saaristo TE, Salomaa V, Saltevo J, Samuel M, Sanghera DK, Saramies J, Scott J, Scott LJ, Scott RA, Segrè AV, Sehmi J, Sennblad B, Shah N, Shah S, Shera AS, Shu XO, Shuldiner AR, Sigurđsson G, Sijbrands E, Silveira A, Sim X, Sivapalaratnam S, Small KS, So WY, Stančáková A, Stefansson K, Steinbach G, Steinthorsdottir V, Stirrups K, Strawbridge RJ, Stringham HM, Sun Q, Suo C, Syvänen AC, Takayanagi R, Takeuchi F, Tay WT, Teslovich TM, Thorand B, Thorleifsson G, Thorsteinsdottir U, Tikkanen E, Trakalo J, Tremoli E, Trip MD, Tsai FJ, Tuomi T, Tuomilehto J, Uitterlinden AG, Valladares-Salgado A, Vedantam S, Veglia F, Voight BF, Wang C, Wareham NJ, Wennauer R, Wickremasinghe AR, Wilsgaard T, Wilson JF, Wiltshire S, Winckler W, Wong TY, Wood AR, Wu JY, Wu Y, Yamamoto K, Yamauchi T, Yang M, Yengo L, Yokota M, Young R, Zabaneh D, Zhang F, Zhang R, Zheng W, Zimmet PZ, Altshuler D, Bowden DW, Cho YS, Cox NJ, Cruz M, Hanis CL, Kooner J, Lee JY, Seielstad M, Teo YY, Boehnke M, Parra EJ, Chambers JC, Tai ES, McCarthy MI, Morris AP. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet . 2014; 46( 3): 234– 244. Google Scholar CrossRef Search ADS PubMed 23. Zhu L, Huang Q, Xie Z, Kang M, Ding H, Chen B, Chen Y, Liu C, Wang Y, Tang W. PPARGC1A rs3736265 G>A polymorphism is associated with decreased risk of type 2 diabetes mellitus and fasting plasma glucose level. Oncotarget . 2017; 8( 23): 37308– 37320. Google Scholar PubMed 24. Esterbauer H, Oberkofler H, Linnemayr V, Iglseder B, Hedegger M, Wolfsgruber P, Paulweber B, Fastner G, Krempler F, Patsch W. Peroxisome proliferator-activated receptor-γ coactivator-1 gene locus: associations with obesity indices in middle-aged women. Diabetes . 2002; 51( 4): 1281– 1286. Google Scholar CrossRef Search ADS PubMed 25. Pihlajamäki J, Kinnunen M, Ruotsalainen E, Salmenniemi U, Vauhkonen I, Kuulasmaa T, Kainulainen S, Laakso M. Haplotypes of PPARGC1A are associated with glucose tolerance, body mass index and insulin sensitivity in offspring of patients with type 2 diabetes. Diabetologia . 2005; 48( 7): 1331– 1334. Google Scholar CrossRef Search ADS PubMed 26. Altshuler D, Hirschhorn JN, Klannemark M, Lindgren CM, Vohl MC, Nemesh J, Lane CR, Schaffner SF, Bolk S, Brewer C, Tuomi T, Gaudet D, Hudson TJ, Daly M, Groop L, Lander ES. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet . 2000; 26( 1): 76– 80. Google Scholar CrossRef Search ADS PubMed 27. Ruchat SM, Weisnagel SJ, Vohl MC, Rankinen T, Bouchard C, Perusse L. Evidence for interaction between PPARG Pro12Ala and PPARGC1A Gly482Ser polymorphisms in determining type 2 diabetes intermediate phenotypes in overweight subjects. J Nutrigenet Nutrigenomics . 2009; 117: 455– 459. 28. Shah P, Mudaliar S. Pioglitazone: side effect and safety profile. Expert Opin Drug Saf . 2010; 9( 2): 347– 354. Google Scholar CrossRef Search ADS PubMed 29. Glintborg D, Andersen M, Hagen C, Heickendorff L, Hermann AP. Association of pioglitazone treatment with decreased bone mineral density in obese premenopausal patients with polycystic ovary syndrome: a randomized, placebo-controlled trial. J Clin Endocrinol Metab . 2008; 93( 5): 1696– 1701. Google Scholar CrossRef Search ADS PubMed 30. Lewis JD, Habel LA, Quesenberry CP, Strom BL, Peng T, Hedderson MM, Ehrlich SF, Mamtani R, Bilker W, Vaughn DJ, Nessel L, Van Den Eeden SK, Ferrara A. Pioglitazone use and risk of bladder cancer and other common cancers in persons with diabetes. JAMA . 2015; 314( 3): 265– 277. Google Scholar CrossRef Search ADS PubMed 31. Andrulionytė L, Peltola P, Chiasson J-L, Laakso M; STOP-NIDDM Study Group. Single nucleotide polymorphisms of PPARD in combination with the Gly482Ser substitution of PGC-1A and the Pro12Ala substitution of PPARG2 predict the conversion from impaired glucose tolerance to type 2 diabetes: the STOP-NIDDM trial. Diabetes . 2006; 55( 7): 2148– 2152. Google Scholar CrossRef Search ADS PubMed 32. Andrulionytè L, Zacharova J, Chiasson JL, Laakso M; STOP-NIDDM Study Group. Common polymorphisms of the PPAR-gamma2 (Pro12Ala) and PGC-1alpha (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial. Diabetologia . 2004; 47( 12): 2176– 2184. Google Scholar CrossRef Search ADS PubMed 33. Kaul N, Singh YP, Bhanwer AJS. The influence of ethnicity in the association of WC, WHR, hypertension and PGC-1α (Gly482Ser), UCP2 -866 G/A and SIRT1 -1400 T/C polymorphisms with T2D in the population of Punjab. Gene . 2015; 563( 2): 150– 154. Google Scholar CrossRef Search ADS PubMed 34. Pei X, Liu L, Cai J, Wei W, Shen Y, Wang Y, Chen Y, Sun P, Imam MU, Ping Z, Fu X. Haplotype-based interaction of the PPARGC1A and UCP1 genes is associated with impaired fasting glucose or type 2 diabetes mellitus. Medicine (Baltimore) . 2017; 96( 23): e6941. Google Scholar CrossRef Search ADS PubMed 35. Stefan N, Thamer C, Staiger H, Machicao F, Machann J, Schick F, Venter C, Niess A, Laakso M, Fritsche A, Häring HU. Genetic variations in PPARD and PPARGC1A determine mitochondrial function and change in aerobic physical fitness and insulin sensitivity during lifestyle intervention. J Clin Endocrinol Metab . 2007; 92( 5): 1827– 1833. Google Scholar CrossRef Search ADS PubMed 36. Rai E, Sharma S, Koul A, Bhat AK, Bhanwer AJ, Bamezai RN. Interaction between the UCP2-866G/A, mtDNA 10398G/A and PGC1alpha p.Thr394Thr and p.Gly482Ser polymorphisms in type 2 diabetes susceptibility in North Indian population. Hum Genet . 2007; 122( 5): 535– 540. Google Scholar CrossRef Search ADS PubMed 37. Thamer C, Machann J, Stefan N, Schäfer SA, Machicao F, Staiger H, Laakso M, Böttcher M, Claussen C, Schick F, Fritsche A, Haring HU. Variations in PPARD determine the change in body composition during lifestyle intervention: a whole-body magnetic resonance study. J Clin Endocrinol Metab . 2008; 93( 4): 1497– 1500. Google Scholar CrossRef Search ADS PubMed 38. Lin YC, Chang PF, Chang MH, Ni YH. A common variant in the peroxisome proliferator-activated receptor-γ coactivator-1α gene is associated with nonalcoholic fatty liver disease in obese children. Am J Clin Nutr . 2013; 97( 2): 326– 331. Google Scholar CrossRef Search ADS PubMed 39. Tai CM, Huang CK, Tu HP, Hwang JC, Yeh ML, Huang CF, Huang JF, Dai CY, Chuang WL, Yu ML. Interactions of a PPARGC1A variant and a PNPLA3 variant affect nonalcoholic steatohepatitis in severely obese Taiwanese patients. Medicine (Baltimore) . 2016; 95( 12): e3120. Google Scholar CrossRef Search ADS PubMed 40. Povel CM, Feskens EJ, Imholz S, Blaak EE, Boer JM, Dollé ME. Glucose levels and genetic variants across transcriptional pathways: interaction effects with BMI. Int J Obes . 2010; 34( 5): 840– 845. Google Scholar CrossRef Search ADS 41. Franks PW, Christophi CA, Jablonski KA, Billings LK, Delahanty LM, Horton ES, Knowler WC, Florez JC; Diabetes Prevention Program Research Group. Common variation at PPARGC1A/B and change in body composition and metabolic traits following preventive interventions: the Diabetes Prevention Program. Diabetologia . 2014; 57( 3): 485– 490. Google Scholar CrossRef Search ADS PubMed 42. Ha CD, Cho JK, Han T, Lee SH, Kang HS. Relationship of PGC-1alpha gene polymorphism with insulin resistance syndrome in Korean children. Asia-Pac J Public Health . 2015; 27( 2): NP544– NP551 Google Scholar CrossRef Search ADS PubMed 43. Weng SW, Lin TK, Wang PW, Chen IY, Lee HC, Chen SD, Chuang YC, Liou CW. Gly482Ser polymorphism in the peroxisome proliferator-activated receptor gamma coactivator-1alpha gene is associated with oxidative stress and abdominal obesity. Metabolism . 2010; 59( 4): 581– 586. Google Scholar CrossRef Search ADS PubMed 44. Vázquez-Del Mercado M, Guzmán-Ornelas M-O, Corona Meraz F-I, Ríos-Ibarra C-P, Reyes-Serratos E-A, Castro-Albarran J, Ruíz-Quezada S-L, Navarro-Hernández R-E. The 482Ser of PPARGC1A and 12Pro of PPARG2 alleles are associated with reduction of metabolic risk factors even obesity in a Mexican-Mestizo population. BioMed Res Int . 2015; 2015: 285491 Google Scholar PubMed 45. Deeb SS, Brunzell JD. The role of the PGC1α Gly482Ser polymorphism in weight gain due to intensive diabetes therapy. PPAR Res . 2009; 2009: 649286 Google Scholar CrossRef Search ADS PubMed 46. Ambye L, Rasmussen S, Fenger M, Jørgensen T, Borch-Johnsen K, Madsbad S, Urhammer SA. Studies of the Gly482Ser polymorphism of the peroxisome proliferator-activated receptor gamma coactivator 1alpha (PGC-1alpha) gene in Danish subjects with the metabolic syndrome. Diabetes Res Clin Pract . 2005; 67( 2): 175– 179. Google Scholar CrossRef Search ADS PubMed 47. Vimaleswaran KS, Radha V, Anjana M, Deepa R, Ghosh S, Majumder PP, Rao MRS, Mohan V. Effect of polymorphisms in the PPARGC1A gene on body fat in Asian Indians. Int J Obes . 2006; 30( 6): 884– 891. Google Scholar CrossRef Search ADS 48. Franks PW, Ekelund U, Brage S, Luan J, Schafer AJ, O’Rahilly S, Barroso I, Wareham NJ. PPARGC1A coding variation may initiate impaired NEFA clearance during glucose challenge. Diabetologia . 2007; 50( 3): 569– 573. Google Scholar CrossRef Search ADS PubMed 49. Goyenechea E, Crujeiras AB, Abete I, Parra D, Martínez JA. Enhanced short-term improvement of insulin response to a low-caloric diet in obese carriers the Gly482Ser variant of the PGC-1alpha gene. Diabetes Res Clin Pract . 2008; 82( 2): 190– 196. Google Scholar CrossRef Search ADS PubMed 50. Geloneze SR, Geloneze B, Morari J, Matos-Souza JR, Lima MM, Chaim EA, Pareja JC, Velloso LA. PGC1α gene Gly482Ser polymorphism predicts improved metabolic, inflammatory and vascular outcomes following bariatric surgery. Int J Obes . 2012; 36( 3): 363– 368. Google Scholar CrossRef Search ADS 51. Zhang KH, Huang Q, Dai XP, Yin JY, Zhang W, Zhou G, Zhou HH, Liu ZQ. Effects of the peroxisome proliferator activated receptor-γ coactivator-1α (PGC-1α) Thr394Thr and Gly482Ser polymorphisms on rosiglitazone response in Chinese patients with type 2 diabetes mellitus. J Clin Pharmacol . 2010; 50( 9): 1022– 1030. Google Scholar CrossRef Search ADS PubMed 52. Ghosh S, Lertwattanarak R, Lefort N, Molina-Carrion M, Joya-Galeana J, Bowen BP, Garduno-Garcia JJ, Abdul-Ghani M, Richardson A, DeFronzo RA, Mandarino L, Van Remmen H, Musi N. Reduction in reactive oxygen species production by mitochondria from elderly subjects with normal and impaired glucose tolerance. Diabetes . 2011; 60( 8): 2051– 2060. Google Scholar CrossRef Search ADS PubMed 53. Hammarstedt A, Jansson PA, Wesslau C, Yang X, Smith U. Reduced expression of PGC-1 and insulin-signaling molecules in adipose tissue is associated with insulin resistance. Biochem Biophys Res Commun . 2003; 301( 2): 578– 582. Google Scholar CrossRef Search ADS PubMed 54. Patti ME, Butte AJ, Crunkhorn S, Cusi K, Berria R, Kashyap S, Miyazaki Y, Kohane I, Costello M, Saccone R, Landaker EJ, Goldfine AB, Mun E, DeFronzo R, Finlayson J, Kahn CR, Mandarino LJ. Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: potential role of PGC1 and NRF1. Proc Natl Acad Sci USA . 2003; 100( 14): 8466– 8471. Google Scholar CrossRef Search ADS PubMed 55. Leone TC, Lehman JJ, Finck BN, Schaeffer PJ, Wende AR, Boudina S, Courtois M, Wozniak DF, Sambandam N, Bernal-Mizrachi C, Chen Z, Holloszy JO, Medeiros DM, Schmidt RE, Saffitz JE, Abel ED, Semenkovich CF, Kelly DP. PGC-1alpha deficiency causes multi-system energy metabolic derangements: muscle dysfunction, abnormal weight control and hepatic steatosis. PLoS Biol . 2005; 3( 4): e101. Google Scholar CrossRef Search ADS PubMed 56. Lin J, Wu PH, Tarr PT, Lindenberg KS, St-Pierre J, Zhang CY, Mootha VK, Jäger S, Vianna CR, Reznick RM, Cui L, Manieri M, Donovan MX, Wu Z, Cooper MP, Fan MC, Rohas LM, Zavacki AM, Cinti S, Shulman GI, Lowell BB, Krainc D, Spiegelman BM. Defects in adaptive energy metabolism with CNS-linked hyperactivity in PGC-1alpha null mice. Cell . 2004; 119( 1): 121– 135. Google Scholar CrossRef Search ADS PubMed 57. Besse-Patin A, Léveillé M, Oropeza D, Nguyen BN, Prat A, Estall JL. Estrogen signals through peroxisome proliferator-activated receptor-γ coactivator 1α to reduce oxidative damage associated with diet-induced fatty liver disease. Gastroenterology . 2017; 152( 1): 243– 256. Google Scholar CrossRef Search ADS PubMed 58. Estall JL, Kahn M, Cooper MP, Fisher FM, Wu MK, Laznik D, Qu L, Cohen DE, Shulman GI, Spiegelman BM. Sensitivity of lipid metabolism and insulin signaling to genetic alterations in hepatic peroxisome proliferator-activated receptor-gamma coactivator-1alpha expression. Diabetes . 2009; 58( 7): 1499– 1508. Google Scholar CrossRef Search ADS PubMed 59. Yoon JC, Xu G, Deeney JT, Yang S-N, Rhee J, Puigserver P, Levens AR, Yang R, Zhang C-Y, Lowell BB, Berggren P-O, Newgard CB, Bonner-Weir S, Weir G, Spiegelman BM. Suppression of β cell energy metabolism and insulin release by PGC-1α. Dev Cell . 2003; 5( 1): 73– 83. Google Scholar CrossRef Search ADS PubMed 60. Valtat B, Riveline J-P, Zhang P, Singh-Estivalet A, Armanet M, Venteclef N, Besseiche A, Kelly DP, Tronche F, Ferré P, Gautier J- F, Bréant B, Blondeau B. Fetal PGC-1α overexpression programs adult pancreatic β-cell dysfunction. Diabetes . 2013; 62( 4): 1206– 1216. Google Scholar CrossRef Search ADS PubMed 61. Barroso I, Luan J, Sandhu MS, Franks PW, Crowley V, Schafer AJ, O’Rahilly S, Wareham NJ. Meta-analysis of the Gly482Ser variant in PPARGC1A in type 2 diabetes and related phenotypes. Diabetologia . 2006; 49( 3): 501– 505. Google Scholar CrossRef Search ADS PubMed 62. Wang S, Fu C, Wang H, Shi Y, Xu X, Chen J, Song X, Sun K, Wang J, Fan X, Wang H, Yang X, Huan T, Hui R. Polymorphisms of the peroxisome proliferator-activated receptor-γ coactivator-1α gene are associated with hypertrophic cardiomyopathy and not with hypertension hypertrophy. Chin Med J (Engl) . 2008; 12(1): 27– 32. 63. Su Y, Peng SB, Li ZQ, Huang QY. [Association study between PPARGC1A Thr394Thr/ Gly482Ser polymorphisms and type 2 diabetes]. Yi Chuan . 2008; 30( 3): 304– 308. Google Scholar CrossRef Search ADS PubMed 64. Rojek A, Cielecka-Prynda M, Przewlocka-Kosmala M, Laczmanski L, Mysiak A, Kosmala W. Impact of the PPARGC1A Gly482Ser polymorphism on left ventricular structural and functional abnormalities in patients with hypertension. J Hum Hypertens . 2014; 28( 9): 557– 563. Google Scholar CrossRef Search ADS PubMed 65. Zhang Y, Xu W, Li X, Tang Y, Xie P, Ji Y, Fan L, Chen Q. Association between PPARGC1A gene polymorphisms and coronary artery disease in a Chinese population. Clin Exp Pharmacol Physiol . 2008; 35( 10): 1172– 1177. Google Scholar CrossRef Search ADS PubMed 66. Lai C-Q, Tucker KL, Parnell LD, Adiconis X, García-Bailo B, Griffith J, Meydani M, Ordovás JM. PPARGC1A variation associated with DNA damage, diabetes, and cardiovascular diseases: the Boston Puerto Rican Health Study. Diabetes . 2008; 57( 4): 809– 816. Google Scholar CrossRef Search ADS PubMed 67. Prior SL, Clark AR, Jones DA, Bain SC, Hurel SJ, Humphries SE, Stephens JW. Association of the PGC-1α rs8192678 variant with microalbuminuria in subjects with type 2 diabetes mellitus. Dis Markers . 2012; 32( 6): 363– 369. Google Scholar CrossRef Search ADS PubMed 68. Ballestri S, Zona S, Targher G, Romagnoli D, Baldelli E, Nascimbeni F, Roverato A, Guaraldi G, Lonardo A. Nonalcoholic fatty liver disease is associated with an almost twofold increased risk of incident type 2 diabetes and metabolic syndrome. Evidence from a systematic review and meta-analysis. J Gastroenterol Hepatol . 2016; 31( 5): 936– 944. Google Scholar CrossRef Search ADS PubMed 69. Gancheva S, Bierwagen A, Kaul K, Herder C, Nowotny P, Kahl S, Giani G, Klueppelholz B, Knebel B, Begovatz P, Strassburger K, Al-Hasani H, Lundbom J, Szendroedi J, Roden M; German Diabetes Study (GDS) Group. Variants in genes controlling oxidative metabolism contribute to lower hepatic ATP independent of liver fat content in type 1 diabetes. Diabetes . 2016; 65( 7): 1849– 1857. Google Scholar CrossRef Search ADS PubMed 70. Petrovič MG, Kunej T, Peterlin B, Dovč P, Petrovič D. Gly482Ser polymorphism of the peroxisome proliferator-activated receptor-γ coactivator-1 gene might be a risk factor for diabetic retinopathy in Slovene population (Caucasians) with type 2 diabetes and the Pro12Ala polymorphism of the PPARgamma gene is not. Diabetes Metab Res Rev . 2005; 21( 5): 470– 474. Google Scholar CrossRef Search ADS PubMed 71. Gayathri SB, Radha V, Vimaleswaran KS, Mohan V. Association of the PPARGC1A gene polymorphism with diabetic nephropathy in an Asian Indian population (CURES-41). Metab Syndr Relat Disord . 2010; 8( 2): 119– 126. Google Scholar CrossRef Search ADS PubMed 72. Jung L, Suh J, Kim M, Chung K, Moon JY, Lee S, Lee T, Lim C. The polymorphisms of PPAR-gamma coactivator 1alpha Gly482Ser (PGC-1alpha Gly482Ser) are associated with the nephropathy of Korean patients with type 2 diabetes mellitus. Korean J Nephrol . 2006; 25( 5): 753– 759. 73. Chand S, McKnight AJ, Shabir S, Chan W, McCaughan JA, Maxwell AP, Harper L, Borrows R. Analysis of single nucleotide polymorphisms implicate mTOR signalling in the development of new-onset diabetes after transplantation. BBA Clin . 2016; 5: 41– 45. Google Scholar CrossRef Search ADS PubMed 74. Wu H-H, Liu N-J, Yang Z, Tao X-M, Du Y-P, Wang X-C, Lu B, Zhang Z-Y, Hu R-M, Wen J. Association and interaction analysis of PPARGC1A and serum uric acid on type 2 diabetes mellitus in Chinese Han population. Diabetol Metab Syndr . 2014; 6( 1): 107. Google Scholar CrossRef Search ADS PubMed 75. Radcliffe NJ, Seah JM, Clarke M, MacIsaac RJ, Jerums G, Ekinci EI. Clinical predictive factors in diabetic kidney disease progression. J Diabetes Investig . 2017; 8( 1): 6– 18. Google Scholar CrossRef Search ADS PubMed 76. Ingelsson E, Bennet L, Ridderstråle M, Söderström M, Råstam L, Lindblad U. The PPARGC1A Gly482Ser polymorphism is associated with left ventricular diastolic dysfunction in men. BMC Cardiovasc Disord . 2008; 8( 1): 37. Google Scholar CrossRef Search ADS PubMed 77. Cheurfa N, Reis AF, Dubois-Laforgue D, Bellanné-Chantelot C, Timsit J, Velho G. The Gly482Ser polymorphism in the peroxisome proliferator-activated receptor-gamma coactivator-1 gene is associated with hypertension in type 2 diabetic men. Diabetologia . 2004; 47( 11): 1980– 1983. Google Scholar CrossRef Search ADS PubMed 78. Okauchi Y, Iwahashi H, Okita K, Yuan M, Matsuda M, Tanaka T, Miyagawa J, Funahashi T, Horikawa Y, Shimomura I, Yamagata K. PGC-1alpha Gly482Ser polymorphism is associated with the plasma adiponectin level in type 2 diabetic men. Endocr J . 2008; 55( 6): 991– 997. Google Scholar CrossRef Search ADS PubMed 79. Ridderstråle M, Johansson LE, Rastam L, Lindblad U. Increased risk of obesity associated with the variant allele of the PPARGC1A Gly482Ser polymorphism in physically inactive elderly men. Diabetologia . 2006; 49( 3): 496– 500. Google Scholar CrossRef Search ADS PubMed 80. Morselli E, Fuente-Martin E, Finan B, Kim M, Frank A, Garcia-Caceres C, Navas CR, Gordillo R, Neinast M, Kalainayakan SP, Li DL, Gao Y, Yi CX, Hahner L, Palmer BF, Tschöp MH, Clegg DJ. Hypothalamic PGC-1α protects against high-fat diet exposure by regulating ERα. Cell Reports . 2014; 9( 2): 633– 645. Google Scholar CrossRef Search ADS PubMed 81. Soyal S, Krempler F, Oberkofler H, Patsch W. PGC-1alpha: a potent transcriptional cofactor involved in the pathogenesis of type 2 diabetes. Diabetologia . 2006; 49( 7): 1477– 1488. Google Scholar CrossRef Search ADS PubMed 82. Kunej T, Globocnik Petrovic M, Dovc P, Peterlin B, Petrovic D. A Gly482Ser polymorphism of the peroxisome proliferator-activated receptor-gamma coactivator-1 (PGC-1) gene is associated with type 2 diabetes in Caucasians. Folia Biol (Praha) . 2004; 50( 5): 157– 158. Google Scholar PubMed 83. Myles S, Lea RA, Ohashi J, Chambers GK, Weiss JG, Hardouin E, Engelken J, Macartney-Coxson DP, Eccles DA, Naka I, Kimura R, Inaoka T, Matsumura Y, Stoneking M. Testing the thrifty gene hypothesis: the Gly482Ser variant in PPARGC1A is associated with BMI in Tongans. BMC Med Genet . 2011; 12( 1): 10. Google Scholar CrossRef Search ADS PubMed 84. Cadzow M, Merriman TR, Boocock J, Dalbeth N, Stamp LK, Black MA, Visscher PM, Wilcox PL. Lack of direct evidence for natural selection at the candidate thrifty gene locus, PPARGC1A. BMC Med Genet . 2016; 17( 1): 80. Google Scholar CrossRef Search ADS PubMed 85. Bhat A, Koul A, Rai E, Sharma S, Dhar MK, Bamezai RN. PGC-1alpha Thr394Thr and Gly482Ser variants are significantly associated with T2DM in two North Indian populations: a replicate case-control study. Hum Genet . 2007; 121( 5): 609– 614. Google Scholar CrossRef Search ADS PubMed 86. Shokouhi S, Haghani K, Borji P, Bakhtiyari S. Association between PGC-1alpha gene polymorphisms and type 2 diabetes risk: a case-control study of an Iranian population. Can J Diabetes . 2015; 39( 1): 65– 72. Google Scholar CrossRef Search ADS PubMed 87. Sun L, Yang Z, Jin F, Zhu XQ, Qu YC, Shi XH, Wang L. The Gly482Ser variant of the PPARGC1 gene is associated with Type 2 diabetes mellitus in northern Chinese, especially men. Diabet Med . 2006; 23( 10): 1085– 1092. Google Scholar CrossRef Search ADS PubMed 88. Jemaa Z, Kallel A, Sleimi C, Mahjoubi I, Feki M, Ftouhi B, Slimane H, Jemaa R, Kaabachi N. The Gly482Ser polymorphism of the peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) is associated with type 2 diabetes in Tunisian population. Diabetes Metab Syndr . 2015; 9( 4): 316– 319. Google Scholar CrossRef Search ADS PubMed 89. Yang Y, Mo X, Chen S, Lu X, Gu D. Association of peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A) gene polymorphisms and type 2 diabetes mellitus: a meta-analysis. Diabetes Metab Res Rev . 2011; 27( 2): 177– 184. Google Scholar CrossRef Search ADS PubMed 90. Vohl MC, Houde A, Lebel S, Hould FS, Marceau P. Effects of the peroxisome proliferator-activated receptor-gamma co-activator-1 Gly482Ser variant on features of the metabolic syndrome. Mol Genet Metab . 2005; 86( 1-2): 300– 306. Google Scholar CrossRef Search ADS PubMed 91. Nelson TL, Fingerlin TE, Moss LK, Barmada MM, Ferrell RE, Norris JM. Association of the peroxisome proliferator-activated receptor gamma gene with type 2 diabetes mellitus varies by physical activity among non-Hispanic whites from Colorado. Metabolism . 2007; 56( 3): 388– 393. Google Scholar CrossRef Search ADS PubMed 92. Vimaleswaran KS, Radha V, Ghosh S, Majumder PP, Deepa R, Babu HN, Rao MR, Mohan V. Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PGC-1alpha) gene polymorphisms and their relationship to type 2 diabetes in Asian Indians. Diabet Med . 2005; 22( 11): 1516– 1521. Google Scholar CrossRef Search ADS PubMed 93. Jing C, Xueyao H, Linong J. Meta-analysis of association studies between five candidate genes and type 2 diabetes in Chinese Han population. Endocrine . 2012; 42( 2): 307– 320. Google Scholar CrossRef Search ADS PubMed 94. Zhu S, Liu Y, Wang X, Wu X, Zhu X, Li J, Ma J, Gu HF, Liu Y. Evaluation of the association between the PPARGC1A genetic polymorphisms and type 2 diabetes in Han Chinese population. Diabetes Res Clin Pract . 2009; 86( 3): 168– 172. Google Scholar CrossRef Search ADS PubMed 95. Zhang SL, Lu WS, Yan L, Wu MC, Xu MT, Chen LH, Cheng H. Association between peroxisome proliferator-activated receptor-gamma coactivator-1alpha gene polymorphisms and type 2 diabetes in southern Chinese population: role of altered interaction with myocyte enhancer factor 2C. Chin Med J (Engl) . 2007; 120( 21): 1878– 1885. Google Scholar PubMed 96. Wang YB, Yu YC, Li Z, Wang C, Wang JY, Wu GT. [Study on the relationship between polymorphisms of peroxisome proliferators-activated receptor-gamma coactivator-1alpha gene and type 2 diabetes in Shanghai Hans in China]. Zhonghua Yi Xue Yi Chuan Xue Za Zhi . 2005; 22( 4): 453– 456. Google Scholar PubMed 97. Melloul D, Stoffel M. Regulation of transcriptional coactivator PGC-1-alpha. Sci Aging Knowledge Enviro . 2004; 2004( 9): pe9. 98. Sano M, Tokudome S, Shimizu N, Yoshikawa N, Ogawa C, Shirakawa K, Endo J, Katayama T, Yuasa S, Ieda M, Makino S, Hattori F, Tanaka H, Fukuda K. Intramolecular control of protein stability, subnuclear compartmentalization, and coactivator function of peroxisome proliferator-activated receptor gamma coactivator 1alpha. J Biol Chem . 2007; 282( 35): 25970– 25980. Google Scholar CrossRef Search ADS PubMed 99. Akimoto T, Pohnert SC, Li P, Zhang M, Gumbs C, Rosenberg PB, Williams RS, Yan Z. Exercise stimulates Pgc-1alpha transcription in skeletal muscle through activation of the p38 MAPK pathway. J Biol Chem . 2005; 280( 20): 19587– 19593. Google Scholar CrossRef Search ADS PubMed 100. Akimoto T, Ribar TJ, Williams RS, Yan Z. Skeletal muscle adaptation in response to voluntary running in Ca2+/calmodulin-dependent protein kinase IV-deficient mice. Am J Physiol Cell Physiol . 2004; 287( 5): C1311– C1319. Google Scholar CrossRef Search ADS PubMed 101. Gómez-Ambrosi J, Frühbeck G, Martínez JA. Rapid in vivo PGC-1 mRNA upregulation in brown adipose tissue of Wistar rats by a beta(3)-adrenergic agonist and lack of effect of leptin. Mol Cell Endocrinol . 2001; 176( 1-2): 85– 90. Google Scholar CrossRef Search ADS PubMed 102. Henagan TM, Stewart LK, Forney LA, Sparks LM, Johannsen N, Church TS. PGC1α -1 nucleosome position and splice variant expression and cardiovascular disease risk in overweight and obese individuals. PPAR Res . 2014; 2014: 895734 Google Scholar CrossRef Search ADS PubMed 103. Barrès R, Osler ME, Yan J, Rune A, Fritz T, Caidahl K, Krook A, Zierath JR. Non-CpG methylation of the PGC-1alpha promoter through DNMT3B controls mitochondrial density. Cell Metab . 2009; 10( 3): 189– 198. Google Scholar CrossRef Search ADS PubMed 104. Xie X, Gao H, Zeng W, Chen S, Feng L, Deng D, Qiao FY, Liao L, McCormick K, Ning Q, Luo X. Placental DNA methylation of peroxisome-proliferator-activated receptor-γ co-activator-1α promoter is associated with maternal gestational glucose level. Clin Sci (Lond) . 2015; 129( 4): 385– 394. Google Scholar CrossRef Search ADS PubMed 105. Laker RC, Lillard TS, Okutsu M, Zhang M, Hoehn KL, Connelly JJ, Yan Z. Exercise prevents maternal high-fat diet-induced hypermethylation of the Pgc-1α gene and age-dependent metabolic dysfunction in the offspring. Diabetes . 2014; 63( 5): 1605– 1611. Google Scholar CrossRef Search ADS PubMed 106. Li X, Monks B, Ge Q, Birnbaum MJ. Akt/PKB regulates hepatic metabolism by directly inhibiting PGC-1alpha transcription coactivator. Nature . 2007; 447( 7147): 1012– 1016. Google Scholar CrossRef Search ADS PubMed 107. Rodgers JT, Haas W, Gygi SP, Puigserver P. Cdc2-like kinase 2 is an insulin-regulated suppressor of hepatic gluconeogenesis. Cell Metab . 2010; 11( 1): 23– 34. Google Scholar CrossRef Search ADS PubMed 108. Olson BL, Hock MB, Ekholm-Reed S, Wohlschlegel JA, Dev KK, Kralli A, Reed SI. SCFCdc4 acts antagonistically to the PGC-1α transcriptional coactivator by targeting it for ubiquitin-mediated proteolysis. Genes Dev . 2008; 22( 2): 252– 264. Google Scholar CrossRef Search ADS PubMed 109. Handschin C, Spiegelman BM. Peroxisome proliferator-activated receptor γ coactivator 1 coactivators, energy homeostasis, and metabolism. Endocr Rev . 2006; 27( 7): 728– 735. Google Scholar CrossRef Search ADS PubMed 110. Ling C, Poulsen P, Carlsson E, Ridderstråle M, Almgren P, Wojtaszewski J, Beck-Nielsen H, Groop L, Vaag A. Multiple environmental and genetic factors influence skeletal muscle PGC-1α and PGC-1β gene expression in twins. J Clin Invest . 2004; 114( 10): 1518– 1526. Google Scholar CrossRef Search ADS PubMed 111. Nitz I, Ewert A, Klapper M, Döring F. Analysis of PGC-1alpha variants Gly482Ser and Thr612Met concerning their PPARgamma2-coactivation function. Biochem Biophys Res Commun . 2007; 353( 2): 481– 486. Google Scholar CrossRef Search ADS PubMed 112. Chen Y, Mu P, He S, Tang X, Guo X, Li H, Xu H, Woo S-L, Qian X, Zeng L, Wu C. Gly482Ser mutation impairs the effects of peroxisome proliferator-activated receptor γ coactivator-1α on decreasing fat deposition and stimulating phosphoenolpyruvate carboxykinase expression in hepatocytes. Nutr Res . 2013; 33( 4): 332– 339. Google Scholar CrossRef Search ADS PubMed 113. Choi YS, Hong JM, Lim S, Ko KS, Pak YK. Impaired coactivator activity of the Gly482 variant of peroxisome proliferator-activated receptor gamma coactivator-1alpha (PGC-1alpha) on mitochondrial transcription factor A (Tfam) promoter. Biochem Biophys Res Commun . 2006; 344( 3): 708– 712. Google Scholar CrossRef Search ADS PubMed 114. Handschin C, Rhee J, Lin J, Tarr PT, Spiegelman BM. An autoregulatory loop controls peroxisome proliferator-activated receptor gamma coactivator 1alpha expression in muscle. Proc Natl Acad Sci USA . 2003; 100( 12): 7111– 7116. Google Scholar CrossRef Search ADS PubMed Copyright © 2018 Endocrine Society
Endocrinology – Oxford University Press
Published: Feb 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera