OBJECTIVE

Maturity-onset diabetes of the young (MODY) as a result of mutations in hepatocyte nuclear factor 1-α (HNF1A) is often misdiagnosed as type 1 diabetes or type 2 diabetes. Recent work has shown that high-sensitivity C-reactive protein (hs-CRP) levels are lower in HNF1A-MODY than type 1 diabetes, type 2 diabetes, or glucokinase (GCK)-MODY. We aim to replicate these findings in larger numbers and other MODY subtypes.

RESEARCH DESIGN AND METHODS

hs-CRP levels were assessed in 750 patients (220 HNF1A, 245 GCK, 54 HNF4-α [HNF4A], 21 HNF1-β (HNF1B), 53 type 1 diabetes, and 157 type 2 diabetes).

RESULTS

hs-CRP was lower in HNF1A-MODY (median [IQR] 0.3 [0.1–0.6] mg/L) than type 2 diabetes (1.40 [0.60–3.45] mg/L; P < 0.001) and type 1 diabetes (1.10 [0.50–1.85] mg/L; P < 0.001), HNF4A-MODY (1.45 [0.46–2.88] mg/L; P < 0.001), GCK-MODY (0.60 [0.30–1.80] mg/L; P < 0.001), and HNF1B-MODY (0.60 [0.10–2.8] mg/L; P = 0.07). hs-CRP discriminated HNF1A-MODY from type 2 diabetes with hs-CRP <0.75 mg/L showing 79% sensitivity and 70% specificity (receiver operating characteristic area under the curve = 0.84).

CONCLUSIONS

hs-CRP levels are lower in HNF1A-MODY than other forms of diabetes and may be used as a biomarker to select patients for diagnostic HNF1A genetic testing.

OBJECTIVE

Despite the clinical importance of an accurate diagnosis in individuals with monogenic forms of diabetes, restricted access to genetic testing leaves many patients with undiagnosed diabetes. Recently, common variation near the HNF1 homeobox A (HNF1A) gene was shown to influence C-reactive protein levels in healthy adults. We hypothesized that serum levels of high-sensitivity C-reactive protein (hs-CRP) could represent a clinically useful biomarker for the identification of HNF1A mutations causing maturity-onset diabetes of the young (MODY).

RESEARCH DESIGN AND METHODS

Serum hs-CRP was measured in subjects with HNF1A-MODY (n = 31), autoimmune diabetes (n = 316), type 2 diabetes (n = 240), and glucokinase (GCK) MODY (n = 24) and in nondiabetic individuals (n = 198). The discriminative accuracy of hs-CRP was evaluated through receiver operating characteristic (ROC) curve analysis, and performance was compared with standard diagnostic criteria. Our primary analyses excluded ∼11% of subjects in whom the single available hs-CRP measurement was >10 mg/l.

RESULTS

Geometric mean (SD range) hs-CRP levels were significantly lower (P ≤ 0.009) for HNF1A-MODY individuals, 0.20 (0.03–1.14) mg/l, than for any other group: autoimmune diabetes 0.58 (0.10–2.75) mg/l, type 2 diabetes 1.33 (0.28–6.14) mg/l, GCK-MODY 1.01 (0.19–5.33) mg/l, and nondiabetic 0.48 (0.10–2.42) mg/l. The ROC-derived C-statistic for discriminating HNF1A-MODY and type 2 diabetes was 0.8. Measurement of hs-CRP, either alone or in combination with current diagnostic criteria, was superior to current diagnostic criteria alone. Sensitivity and specificity for the combined criteria approached 80%.

CONCLUSIONS

Serum hs-CRP levels are markedly lower in HNF1A-MODY than in other forms of diabetes. hs-CRP has potential as a widely available, cost-effective screening test to support more precise targeting of MODY diagnostic testing.

OBJECTIVE

Assignment of the correct molecular diagnosis in diabetes is necessary for informed decisions regarding treatment and prognosis. Better clinical markers would facilitate discrimination and prioritization for genetic testing between diabetes subtypes. Serum 1,5 anhydroglucitol (1,5AG) levels were reported to differentiate maturity-onset diabetes of the young due to HNF1A mutations (HNF1A-MODY) from type 2 diabetes, but this requires further validation. We evaluated serum 1,5AG in a range of diabetes subtypes as an adjunct for defining diabetes etiology.

RESEARCH DESIGN AND METHODS

1,5AG was measured in U.K. subjects with: HNF1A-MODY (n = 23), MODY due to glucokinase mutations (GCK-MODY, n = 23), type 1 diabetes (n = 29), latent autoimmune diabetes in adults (LADA, n = 42), and type 2 diabetes (n = 206). Receiver operating characteristic curve analysis was performed to assess discriminative accuracy of 1,5AG for diabetes etiology.

RESULTS

Mean (SD range) 1,5AG levels were: GCK-MODY 13.06 μg/ml (5.74–29.74), HNF1A-MODY 4.23 μg/ml (2.12–8.44), type 1 diabetes 3.09 μg/ml (1.45–6.57), LADA 3.46 μg/ml (1.42–8.45), and type 2 diabetes 5.43 (2.12–13.23). Levels in GCK-MODY were higher than in other groups (P < 10−4 vs. each group). HNF1A-MODY subjects showed no difference in unadjusted 1,5AG levels from type 2 diabetes, type 1 diabetes, and LADA. Adjusting for A1C revealed a difference between HNF1A-MODY and type 2 diabetes (P = 0.001). The discriminative accuracy of unadjusted 1,5AG levels was 0.79 for GCK-MODY versus type 2 diabetes and 0.86 for GCK-MODY versus HNF1A-MODY but was only 0.60 for HNF1A-MODY versus type 2 diabetes.

CONCLUSIONS

In our dataset, serum 1,5AG performed well in discriminating GCK-MODY from other diabetes subtypes, particularly HNF1A-MODY. Measurement of 1,5AG levels could inform decisions regarding MODY diagnostic testing.

Background

There is considerable interest in the hypothesis that low frequency, intermediate penetrance variants contribute to the proportion of Type 2 Diabetes (T2D) susceptibility not attributable to the common variants uncovered through genome-wide association approaches. Genes previously implicated in monogenic and multifactorial forms of diabetes are obvious candidates in this respect. In this study, we focussed on exons 8–10 of the HNF1A gene since rare, penetrant mutations in these exons (which are only transcribed in selected HNF1A isoforms) are associated with a later age of diagnosis of Maturity onset diabetes of the young (MODY) than mutations in exons 1–7. The age of diagnosis in the subgroup of HNF1A-MODY individuals with exon 8–10 mutations overlaps with that of early multifactorial T2D, and we set out to test the hypothesis that these exons might also harbour low-frequency coding variants of intermediate penetrance that contribute to risk of multifactorial T2D.

Methodology and Principal Findings

We performed targeted capillary resequencing of HNF1A exons 8–10 in 591 European T2D subjects enriched for genetic aetiology on the basis of an early age of diagnosis (≤45 years) and/or family history of T2D (≥1 affected sibling). PCR products were sequenced and compared to the published HNF1A sequence. We identified several variants (rs735396 [IVS9−24T>C], rs1169304 [IVS8+29T>C], c.1768+44C>T [IVS9+44C>T] and rs61953349 [c.1545G>A, p.T515T] but no novel non-synonymous coding variants were detected.

Conclusions and Significance

We conclude that low frequency, nonsynonymous coding variants in the terminal exons of HNF1A are unlikely to contribute to T2D-susceptibility in European samples. Nevertheless, the rationale for seeking low-frequency causal variants in genes known to contain rare, penetrant mutations remains strong and should motivate efforts to screen other genes in a similar fashion.

Mutations in the LMNA gene (encoding lamin A/C) underlie familial partial lipodystrophy, a syndrome of monogenic insulin resistance and diabetes. LMNA maps to the well-replicated diabetes-linkage region on chromosome 1q, and there are reported associations between LMNA single nucleotide polymorphisms (SNPs) (particularly rs4641; H566H) and metabolic syndrome components. We examined the relationship between LMNA variation and type 2 diabetes (using six tag SNPs capturing >90% of common variation) in several large datasets. Analysis of 2,490 U.K. diabetic case and 2,556 control subjects revealed no significant associations at either genotype or haplotype level: the minor allele at rs4641 was no more frequent in case subjects (allelic odds ratio [OR] 1.07 [95% CI 0.98-1.17], P = 0.15). In 390 U.K. trios, family-based association analyses revealed nominally significant overtransmission of the major allele at rs12063564 (P = 0.01), which was not corroborated in other samples. Finally, genotypes for 2,817 additional subjects from the International 1q Consortium revealed no consistent case-control or family-based associations with LMNA variants. Across all our data, the OR for the rs4641 minor allele approached but did not attain significance (1.07 [0.99-1.15], P = 0.08). Our data do not therefore support a major effect of LMNA variation on diabetes risk. However, in a meta-analysis including other available data, there is evidence that rs4641 has a modest effect on diabetes susceptibility (1.10 [1.04-1.16], P = 0.001).

Background

A limited number of studies have assessed the risk of common diseases when combining information from several predisposing polymorphisms. In most cases, individual polymorphisms only moderately increase risk (~20%), and they are thought to be unhelpful in assessing individuals' risk clinically. The value of analyzing multiple alleles simultaneously is not well studied. This is often because, for any given disease, very few common risk alleles have been confirmed.

Methods and Findings

Three common variants (Lys23 of KCNJ11, Pro12 of PPARG, and the T allele at rs7903146 of TCF7L2) have been shown to predispose to type 2 diabetes mellitus across many large studies. Risk allele frequencies ranged from 0.30 to 0.88 in controls. To assess the combined effect of multiple susceptibility alleles, we genotyped these variants in a large case-control study (3,668 controls versus 2,409 cases). Individual allele odds ratios (ORs) ranged from 1.14 (95% confidence interval [CI], 1.05 to 1.23) to 1.48 (95% CI, 1.36 to 1.60). We found no evidence of gene-gene interaction, and the risks of multiple alleles were consistent with a multiplicative model. Each additional risk allele increased the odds of type 2 diabetes by 1.28 (95% CI, 1.21 to 1.35) times. Participants with all six risk alleles had an OR of 5.71 (95% CI, 1.15 to 28.3) compared to those with no risk alleles. The 8.1% of participants that were double-homozygous for the risk alleles at TCF7L2 and Pro12Ala had an OR of 3.16 (95% CI, 2.22 to 4.50), compared to 4.3% with no TCF7L2 risk alleles and either no or one Glu23Lys or Pro12Ala risk alleles.

Conclusions

Combining information from several known common risk polymorphisms allows the identification of population subgroups with markedly differing risks of developing type 2 diabetes compared to those obtained using single polymorphisms. This approach may have a role in future preventative measures for common, polygenic diseases.

Combining information from several known common risk polymorphisms allows the identification of subgroups of the population with markedly differing risks of developing type 2 diabetes.

Editors' Summary

Background.

Diabetes is an important and increasingly common global health problem; the World Health Organization has estimated that about 170 million people currently have diabetes worldwide. One particular form, type 2 diabetes, develops when cells in the body become unable to respond to a hormone called insulin. Insulin is normally released by the pancreas and controls the ability of body cells to take in glucose (sugar). Therefore, when cells become insensitive to insulin as in people with type 2 diabetes, glucose levels in the body are not well controlled and may become dangerously high in the blood. These high levels can have long-term damaging effects on various organs in the body, particularly the eyes, nerves, heart, and kidneys. There are many different factors that affect whether someone is likely to develop type 2 diabetes. These factors can be broadly grouped into two categories: environmental and genetic. Environmental factors such as obesity, a diet high in sugar, and a sedentary lifestyle are all risk factors for developing type 2 diabetes in later life. Genetically, a number of variants in many different genes may affect the risk of developing the disease. Generally, these gene variants are common in human populations but each gene variant only mildly increases the risk that a person possessing it will get type 2 diabetes.

Why Was This Study Done?

The investigators performing this study wanted to understand how different gene variants combine to affect an individual's risk of getting type 2 diabetes. That is, if a person carries many different variants, does their overall risk increase a lot or only a little?

What Did the Researchers Do and Find?

First, the researchers surveyed the published reports to identify those gene variants for which there was strong evidence of an association with type 2 diabetes. They found mutations in three genes that had been shown reproducibly to be associated with type 2 diabetes in different studies: PPARG (whose product is involved in regulation of fat tissue), KCNJ11 (whose product is involved in insulin production), and TCF7L2 (whose product is thought to be involved in controlling sugar levels). Then, they compared two groups of white people in the UK: 2,409 people with type 2 diabetes (“cases”), and 3,668 people from the general population (“controls”). The researchers compared the two groups to see which individuals possessed which gene variants, and did statistical testing to work out to what extent having particular combinations of the gene variants affected an individual's chance of being a “case” versus a “control.” Their results showed that in the groups studied, having an ever-increasing number of gene variants increased the risk of developing diabetes. The risk that someone with none of the gene variants would develop type 2 diabetes was about 2%, while the chance for someone with all gene variants was about10%.

What Do These Findings Mean?

These results show that the risk of developing type 2 diabetes is greater if an individual possesses all of the gene variants that were examined in this study. The analysis also suggests that using information on all three variants, rather than just one, is likely to be more accurate in predicting future risk. How this genetic information should be used alongside other well-known preventative measures such as altered lifestyle requires further study.

Additional Information.

Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030374.

NHS Direct patient information on diabetes

National Diabetes Information Clearinghouse information on type 2 diabetes

World Health Organization Diabetes Programme

Centers for Disease ControlDiabetes Public Health Resource

It is important to identify patients with Maturity-onset diabetes of the young (MODY) as a molecular diagnosis determines both treatment and prognosis. Genetic testing is currently expensive and many patients are therefore not assessed and are misclassified as having either type 1 or type 2 diabetes. Biomarkers could facilitate the prioritisation of patients for genetic testing. We hypothesised that patients with different underlying genetic aetiologies for their diabetes could have distinct metabolic profiles which may uncover novel biomarkers. The aim of this study was to perform metabolic profiling in urine from patients with MODY due to mutations in the genes encoding glucokinase (GCK) or hepatocyte nuclear factor 1 alpha (HNF1A), type 2 diabetes (T2D) and normoglycaemic control subjects. Urinary metabolic profiling by Nuclear Magnetic Resonance (NMR) and ultra performance liquid chromatography hyphenated to Q-TOF mass spectrometry (UPLC-MS) was performed in a Discovery set of subjects with HNF1A-MODY (n = 14), GCK-MODY (n = 17), T2D (n = 14) and normoglycaemic controls (n = 34). Data were used to build a valid partial least squares discriminate analysis (PLS-DA) model where HNF1A-MODY subjects could be separated from the other diabetes subtypes. No single metabolite contributed significantly to the separation of the patient groups. However, betaine, valine, glycine and glucose were elevated in the urine of HNF1A-MODY subjects compared to the other subgroups. Direct measurements of urinary amino acids and betaine in an extended dataset did not support differences between patients groups. Elevated urinary glucose in HNF1A-MODY is consistent with the previously reported low renal threshold for glucose in this genetic subtype. In conclusion, we report the first metabolic profiling study in monogenic diabetes and show that, despite the distinct biochemical pathways affected, there are unlikely to be robust urinary biomarkers which distinguish monogenic subtypes from T2D. Our results have implications for studies investigating metabolic profiles in complex traits including T2D.

Epidemiological studies consistently show that circulating sex hormone binding globulin (SHBG) levels are lower in type 2 diabetes patients than non-diabetic individuals, but the causal nature of this association is controversial. Genetic studies can help dissect causal directions of epidemiological associations because genotypes are much less likely to be confounded, biased or influenced by disease processes. Using this Mendelian randomization principle, we selected a common single nucleotide polymorphism (SNP) near the SHBG gene, rs1799941, that is strongly associated with SHBG levels. We used data from this SNP, or closely correlated SNPs, in 27 657 type 2 diabetes patients and 58 481 controls from 15 studies. We then used data from additional studies to estimate the difference in SHBG levels between type 2 diabetes patients and controls. The SHBG SNP rs1799941 was associated with type 2 diabetes [odds ratio (OR) 0.94, 95% CI: 0.91, 0.97; P = 2 × 10−5], with the SHBG raising allele associated with reduced risk of type 2 diabetes. This effect was very similar to that expected (OR 0.92, 95% CI: 0.88, 0.96), given the SHBG-SNP versus SHBG levels association (SHBG levels are 0.2 standard deviations higher per copy of the A allele) and the SHBG levels versus type 2 diabetes association (SHBG levels are 0.23 standard deviations lower in type 2 diabetic patients compared to controls). Results were very similar in men and women. There was no evidence that this variant is associated with diabetes-related intermediate traits, including several measures of insulin secretion and resistance. Our results, together with those from another recent genetic study, strengthen evidence that SHBG and sex hormones are involved in the aetiology of type 2 diabetes.

Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes–susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.