GIS Approaches for the Estimation of Residential-Level Ambient PM Concentrations

Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-validations of different kriging models, c) contrast three popular kriging approaches, and d ) calculate SE of the kriging estimations. We used PM data for PM with aerodynamic diameter ≤10 μm (PM10) and aerodynamic diameter ≤ 2.5 μm (PM2.5) from the U.S. Environmental Protection Agency for the year 2000. Kriging estimations were performed at 94,135 geocoded addresses of Women’s Health Initiative study participants using the ArcView geographic information system. We developed a semiautomated program to enable large-scale daily kriging estimation and assessed validity of semivariogram models using prediction error (PE), standardized prediction error (SPE), root mean square standardized (RMSS), and SE of the estimated PM. National- and regional-scale kriging performed satisfactorily, with the former slightly better. The average PE, SPE, and RMSS of daily PM10 semivariograms using regular ordinary kriging with a spherical model were 0.0629, −0.0011, and 1.255 μg/m3, respectively; the average SE of the estimated residential-level PM10 was 27.36 μg/m3. The values for PM2.5 were 0.049, 0.0085, 1.389, and 4.13 μg/m3, respectively. Lognormal ordinary kriging yielded a smaller average SE and effectively eliminated out-of-range predicted values compared to regular ordinary kriging. Semiautomated daily kriging estimations and semivariogram cross-validations are feasible on a national scale. Lognormal ordinary kriging with a spherical model is valid for estimating daily ambient PM at geocoded residential addresses.
Large-scale, population-based epidemiologic investigations of the health effects of ambient air pollution often rely on measurements from a network of air quality monitors maintained by the U.S. Environmental Protection Agency (U.S. EPA 1995a, 1995b, 2005). The Air Quality System (AQS) is the only national ambient air pollution database currently available for public use in the United States. The availability of individual-level health outcome and covariable data from national-scale studies that often characterize participants over the course of several years enables researchers to study the acute effects of ambient air pollution using individual-level data (Liao et al. 2004, 2005a; Sullivan et al. 2005; Wellenius et al. 2005; Whitsel et al. 2004). This approach requires measures of daily particulate matter (PM) exposures, ideally assessed as close to the individual level as possible, such as at participant residences or in immediate proximity to participants themselves. Because daily measures of ambient PM concentrations from the AQS are unavailable in the large majority of locations, spatial estimation methods using geographic information systems (GIS) are increasingly being considered to estimate geocoded location-specific ambient PM concentrations, such as kriging methods. Important methodologic and practical issues still need to be resolved, however. This study was designed to a) assess the feasibility of large-scale kriging estimation of daily residential-level ambient PM concentrations, b) perform and compare cross-validations of different kriging models, c) determine and contrast the most appropriate kriging approaches, and d) calculate the SEs of the kriging estimations.
Materials and Methods
We obtained from AQS the PM10 and PM2.5 (PM with aerodynamic diameter ≤ 10 and 2.5 μm, respectively) data from 1993–2004 (U.S. EPA 2005). The data from 2000 were used for this study after eliminating duplicate records and converting all measures to the same units and denominator. We calculated “monitor-specific” daily averages based on ≥ 18 hourly measures. Monitor-specific daily averages were set to missing for monitors reporting < 18 hourly measures on any given day. If more than one monitor was operating at the same location on a given day, we then computed “site-specific” daily PM10 and PM2.5 averages by taking the mean of the monitors’ measures. We also obtained the longitude and latitude for each site from the AQS database. These data served as pollutant- and site-specific daily source data for our study (Liao et al. 2005b).
We geocoded 94,135 addresses of Women’s Health Initiative (WHI) Clinical Trial (CT) participant residences and examination sites in the contiguous 48 United States and District of Columbia, after assessing geocoding vendor error (Whitsel et al. 2004, 2005). Daily PM10 and PM2.5 concentrations and the associated estimation errors (SEs) are estimated at these geographic locations by the Environmental Epidemiology of Arrhythmogenesis in WHI study (Whitsel 2006).
We used ArcView GIS (version 8.3) and its Geostatistical Analyst Extension (ESRI Inc., Redlands, CA) for semivariogram determination and cross-validation and for subsequent spatial estimation of daily location-specific PM concentrations. Three frequently referenced spatial models (spherical, exponential, Gaussian) (Cressie 1993a; Davis 2002) were considered using the weighted least-squares method (Gribov et al. 2004; Jian et al. 1996) to obtain the “optimal” daily semivariogram parameters (range, partial sill, and nugget). Based on the daily semivariograms, we performed ordinary kriging to estimate the daily mean PM concentration and its SE at each of the 94,135 geocoded addresses. Next, we performed the standard cross-validation—an iterative procedure that omits site-specific PM data points one at a time and refits the model using the remaining data to estimate the PM concentration at the site of the omitted observation. We assessed the validity (also termed “goodness of fit”) of each semivariogram using three cross-validation parameters readily available from the ArcView software package: a) the average of prediction error (PE), where PE is the average of the difference between the predicted and measured daily PM values at each monitoring site; b) the average of standardized prediction error (SPE), where SPE is the PE divided by the SE of estimation across all sites; and c) root mean square standardized (RMSS), the standard deviation (SD) of all SPEs across all sites. Additionally, we assessed the goodness of fit of each semivariogram by the average of the SEs of the estimations, generated by the kriging procedure, across all 94,135 geocoded addresses. The expectations for a good-fitting semivariogram and kriging model are an average PE and SPE near 0, an RMSS near 1, and a small SE. If RMSS < 1, there is a tendency toward overestimation of the variance; if > 1, there is a tendency toward underestimation (ESRI Inc. 2001). These criteria were consistently used to guide our model selection processes throughout this study (Liao et al. 2005c).
As an alternative to using the automatically calculated semivariogram (calculated using the weighted least-squares method (Gribov et al. 2004; Jian et al. 1996), one can also manually specify the semivariogram parameters to improve the cross-validation parameters in ArcView. We selected six least satisfactory daily semivariograms throughout year 2000 and manually adjusted the semivariogram parameters to obtain the best achievable average RMSS and SPE (RMSS as close to 1 and average SPE as close to 0 as possible). The cross-validation parameters from the weighted least-squares method–calculated semivariograms were then compared to those of the manually adjusted semivariograms.
We performed daily ordinary krigings on both the original scale (regular ordinary krig-ing) and the lognormal scale (lognormal ordinary kriging) (Cressie 1993b; Johnston 2001) for all WHI CT addresses for the year 2000 and compared the cross-validation parameters between the two kriging procedures. Log-normal ordinary kriging was used because it has the ability to eliminate the negative predicted values, which is a problem in ordinary kriging, especially when the source data contain extreme values.
Results
Characteristics of the site-specific daily average PM10 and PM2.5 concentrations
During 1994–2003, the number of monitoring sites that provided GIS-usable daily PM10 data varied widely (range, 120–1,340). On 17% of days, GIS-usable data were provided by ≥ 400 monitoring sites; on 39% of days, by 200–400 sites; and on 44% of days, by 120–200 sites. The corresponding values for PM2.5 during 1999–2003 were 33% of days by ≥ 400 sites and 67% of days by 148–400 sites. Specific to the year 2000, there were averages of 325 PM10 and 456 PM2.5 monitoring sites operating per day across the contiguous United States, with minima and maxima of 148 and 1,061 sites for PM10 and 178 and 1,019 sites for PM2.5. As a result, there were 118,791 site-days during 2000 for which we can retrieve measured PM10 data and 166,796 site-days for PM2.5 data. The mean (± SD) of PM10 and PM2.5 from these retrievable site-days were 26.29 ± 58.13 and 13.14 ± 8.59 μg/m3, respectively, with medians of 21.33 and 11.20 μg/m3, respectively. A right-skewed distribution of both PM10 and PM2.5 are evident, especially for PM10. Figure 1 illustrates the spatial relationships between the geocoded addresses and the PM monitoring sites on an optimal day and a typical day. The mean distance between each address and its nearest PM monitor was 12.35 km, with an SD of 13.98 km, a median of 7.81 km, an interquartile range of 10.53 km, and 99th percentile of 68.36 km.
Comparisons of three widely used spatial models
Tables 1 and 2 present summary statistics of the cross-validation parameters (PE, SPE, and RMSS) comparing three widely used spatial models (spherical, exponential, Gaussian) for PM10 and PM2.5, respectively. In general, both average PE and average SPE are very close to 0, with a very narrow range of variation from the 366 daily cross-validations. More specifically, > 95% of average PEs were within ± 2 μg/m3 of measured PM10, and ± 0.5 μg/m3 of measured PM2.5, an average measurement error that we considered acceptable. In terms of RMSS, we considered > 95% of cross-validations as acceptable, but there were days when RMSS indicated a slight over-or underestimation of the prediction variability. These data support the overall validity of using kriging-based estimation approaches to estimate location-specific PM concentrations across the contiguous United States.
Comparisons of default and manually adjusted semivariograms
Table 3 presents the cross-validations and actual kriging estimations from the weighted least-squares mean method calculated semivariogram and manually adjusted semivariogram. For the 6 days when the PE, SPE, or RMSS indicated a less satisfactory default-calculated semivariogram, these three cross-validation parameters could be improved satisfactorily through adjustment of the semivariogram parameters by an operator. However, the application of such “improved” semivariograms to the estimation of PM10 concentrations at geocoded locations across the United States did not necessarily provide better estimation of location-specific PM (i.e., smaller SEs). To the contrary, the average SEs from the default semivariograms were smaller than those from manually adjusted semivariograms. Because each average SPE of the default-calculated daily semivariograms was close to 0, and each default-calculated daily semivariogram produced a smaller estimation error, we recommend using the default-calculated semivariogram, even though the RMSS from the default-calculated semivariogram was not fully satisfactory.
Comparisons of regular versus lognormal ordinary krigings
We applied regular ordinary kriging (spherical model, default-calculated daily semivariograms) to estimate daily PM10 concentrations at geocoded addresses (n = 94,135) of WHI CT participants and examination sites in the contiguous United States. We examined the estimated PM10 concentrations and identified 22 days during 2000 when estimated values exceeded the range of observed values. In some cases, the estimated values were negative. The number of addresses affected by this problem ranged from a few on most days to 3.5% of all addresses. This problem was related to skewed PM10 distributions and to small numbers of extreme outlying values or operating sites on some days. We therefore compared regular ordinary kriging and log-normal ordinary kriging anticipating that log-normal kriging would attenuate this problem.
Table 4 lists the 22 days on which regular ordinary kriging yielded estimated PM10 values that were outside the range of measured values. For comparison, the minima and maxima of the measured and estimated PM10 concentrations from both regular and log-normal ordinary krigings are also listed in Table 4. In summary, during 2000, lognor-mal ordinary kriging effectively reduced the number of problematic days from 22 to 1. Even on this one day, lognormal ordinary kriging yielded a minimum value that was closer to the range of measured data than that from regular ordinary kriging.
Table 5 shows the mean values of cross-validation parameters of daily PM10 semivariograms for both regular ordinary kriging and lognormal ordinary kriging. Cross-validation parameters were within the acceptable range from both regular and lognormal ordinary krigings, except for the 22 “out-of-range” days as defined above. On these out-of-range days, the SPE was well within the acceptable range for both regular and lognormal krigings, but the RMSS was > 1 from both approaches. Even so, for these out-of-range days RMSS from lognormal ordinary kriging was closer to 1 than that from regular ordinary kriging.
We then performed regular and lognormal ordinary kriging to estimate PM10 concentrations at geocoded addresses of WHI CT participants and examination sites, based on year 2000 PM10 data (94,135 locations and 366 days). The mean, SD, median, and maximum of the daily mean SE of the estimated PM10 from the regular ordinary kriging were 27.36, 83.35, 13.93, and 1160.20 μg/m3, respectively. In contrast, those from the lognormal ordinary kriging were 16.29, 6.65, 15.05, and 67.46 μg/m3. Clearly, the distribution of the estimation errors from lognormal ordinary kriging was considerably less skewed and had fewer outlying values than that from regular ordinary kriging. Alternative methods (winsorizing extreme PM10 values; using ArcView’s “no-sector” option to search for measured data points from a circle centered around a location that needs of an estimation—i.e., disabling the default “sector” search for measured data points in the four sectors of a circle, reducing the range or nugget) were less effective in estimating predicted values within the range of measured values (data not shown).
Similar to the situation observed in PM10 estimations, lognormal ordinary kriging also effectively eliminated the negative or out-of-range problem that occurred in about 5% of PM2.5 data when using regular ordinary kriging. Other cross-validation parameters were comparable between the lognormal and regular ordinary krigings (data not shown).
Comparisons between national and regional krigings
From the 61 days when 900 or more monitoring sites were operating in the year 2000 in the 48 contiguous states, the first of such days from each month was selected for comparisons between ordinary kriging models on a national versus regional scale. National krigings and cross-validations were performed on these 12 selected days using daily site-specific PM10 data. Regional krigings and cross-validations were performed on the same data using the regional map (Figure 1) that divides the U.S. continent into five regions (northwest, southwest, middle north, southeast, and northeast). These five regions were created based on the assumption that different semivariogram parameters would be needed for different geographic areas. In general, for both regional and national krigings, the average SPE and RMSS from cross-validations of semivariograms calculated for the 12 selected days were very close to 0 and 1, respectively (Table 6)
Discussion
Classical methods often assume that measures are uniformly or randomly distributed. The assumptions are often inappropriate for analysis of environmental measures because values at neighboring locations are rarely independent, particularly over short distances. This form of dependence (spatial autocorrelation) nonetheless makes it possible to interpolate values at unmonitored locations from known values at monitored locations. Kriging is one such interpolation method originally developed by mining engineers (Krige 1966). It is especially attractive in this setting because it takes the spatial autocorrelation structure function (variogram) into account by considering known values from monitored locations, weighting them with values read from the variogram at corresponding distances, and splitting weights among adjacent locations. The method thereby ensures that interpolations do not depend on monitor density (Legendre and Fortin 1989). By doing so, kriging yields best linear unbiased estimates, in this setting, of location-specific daily mean ambient PM concentrations and their SEs.
Large-scale population-based epidemiologic investigations of the health effects of ambient air pollution often rely on data collected from a network of air quality monitors maintained by the U.S. EPA—the AQS data (U.S. EPA 1995a, 1995b, 2005). It is revealing to compare kriging with interpolation methods used in the well-known time-series and cohort studies of PM effects on mortality and cardiovascular disease (Abbey et al. 1991, 1999; Dockery et al. 1993; Katsouyanni et al. 1996, 2001; Miller et al. 2004, 2005; Pope et al. 2004; Samet et al. 2000a, 2000b). These studies uniformly estimated PM exposures using area-based arithmetic averaging or nearest-neighbor imputation—alternative methods that have important limitations (Moore and Carpenter 1999). Such limitations include the assumption of homogeneous exposures within study areas and the inability (or failure) to estimate exposures or associated PEs. For example, when daily exposure was of interest and there were no operating PM monitors with a study area, data pairs (daily PM concentrations, death counts) were unavailable in these studies. In addition, when longer-term (monthly to yearly) exposure was of interest, area aggregated exposures were based on available measurements within a given time frame. If there were five 24-hr measures in a month, for example, the monthly average exposure was calculated as the mean of the five readings. In contrast, our kriging-based approach estimated daily mean exposures and SEs at geocoded addresses of participants and their examination sites across the contiguous United States that can be readily integrated over time with little influence of missing data. Studies in the geosciences have also found that kriging provides consistently improved interpolation accuracy over traditional inverse-distance weighting and other, simpler spatial interpolation methods (Zimmerman 1999). Another important advantage of GIS-based estimation over the traditional area-average approach is the availability of both the location-specific estimated pollutant concentrations and their SEs.
Our goal in this study was to contribute methodologic and practical insights toward standardized, semiautomated GIS approaches to estimation of daily air pollution concentrations and their associated estimation errors. The air pollution data estimated using these approaches will support the Environmental Epidemiology of Arrhythmogenesis in WHI study (Whitsel 2006) examining the cardiac effects of air pollution in 68,133 post-menopausal women 50–79 years of age at baseline in the WHI CT (WHI Study Group 1998). Here we describe our experience resolving several important methodologic and practical issues in adopting a systematic, standardized, and semiautomated kriging approach to estimate daily air pollution concentrations and the associated estimation errors at geocoded addresses across the contiguous United States over 10 years.
We successfully downloaded from AQS the PM10 and PM2.5 raw data from 1993–2004. We then cleaned, calculated, and reconstructed site-specific daily PM concentration data ready for GIS applications. It is well known that the monitoring sites in AQS are not randomly distributed, which is one of the assumptions in kriging estimation, and the density of the monitoring sites is relatively low given the size of the contiguous United States. However, the AQS is the only currently available nationwide database. Our cross-validation studies suggest that the AQS data can be used as source data for kriging estimation of ambient pollution concentrations at various locations across the 48 contiguous states.
In this study, we performed cross-validation to assess the goodness of fit of various semivariogram and spatial models using four major parameters: the average PE, SPE, RMSS, and SE of estimation. Details can be found elsewhere (Webster and Oliver 2001), but it is worth noting that in addition to using the SE as a measure of the goodness of fit of a kriging model, one could improve the health effects models by incorporating SE in the models to account for the error in the estimation of location-specific PM concentrations. We consider this an important advantage of GIS-based estimation over the traditional area-average approach and are performing studies of using SE in health effects models.
We compared the performance of three widely spatial models (spherical, exponential, Gaussian) for PM10 and PM2.5 estimations using regular ordinary kriging on a national scale (Tables 1 and 2). In general, the cross-validation parameters suggest that all three models performed fairly well. Overall, the spherical model seemed to perform slightly better, consistent with the observation that the spatial distribution pattern of ambient air pollutants is closest to the assumption of the spherical model. The spherical model has been used most often in modeling spatially distributed data, providing a further rationale for its use in our large-scale population-based study of the health effects of PM. Furthermore, from the perspective of the cross-validation results, both average PE and average SPE are very close to 0, with a very narrow range of variation from the 366 daily cross-validations. These data support the overall validity of using kriging-based estimation approaches to estimate location-specific PM concentrations across the contiguous United States.
We completed an empirical analysis to investigate whether manually adjusting semivariogram parameters improves a) cross-validation parameters and b) estimated PM10 concentrations and their SEs (Table 3). From these data, we conclude that manually adjusting semivariogram parameters improves cross-validation parameters. However, the application of such “improved” semivariograms to the estimation of PM10 concentrations at geocoded locations across the United States did not necessarily provide better estimation of location-specific PM. Therefore, we recommend using the default-calculated semivariogram.
Semivariograms are sensitive to strong positive skewness. As a result, regular ordinary kriging can yield negative predicted values or values exceeding the range of the source data. Kriging works best if the input data have a normal distribution. One solution is to log-transform the input data—using “lognormal kriging.” In the ArcView software package, performing lognormal kriging is a standard option. This option log-transforms the input data to normalize its distribution and attenuate the impact of very large values. It also back-transforms the estimated values and the “unbiased” SE of the estimation to the original scale (Cressie 1993b; Johnston 2001). Our results comparing lognormal ordinary kriging versus regular-scale ordinary kriging suggest that lognormal ordinary kriging not only effectively estimated location-specific PM concentrations within the range of the measured data for the days regular ordinary kriging yielded negative or “out of range” PM estimations, but also yielded a smaller average SE than did regular ordinary kriging and estimations. Therefore, our results support the use of lognormal ordinary kriging as an acceptable solution to the problem commonly posed by positively skewed distributions of environmental data.
Our comparisons of national- versus regional-scale kriging indicate that, in terms of cross-validation results, both performed similarly. However, such comparisons are based on krigings using the source data from optimal days (when > 900 sites across the country were reporting data), which account for only 17% of all days in a year. Therefore, there is additional justification for using national-scale kriging: Usually, there were very few operating sites within a region. On typical days—when only about 200 monitoring sites were operating—ability to derive stable and meaningful semivariograms was greatly impaired. Regional kriging also poses problems for estimation at locations near regional borders. For example, at locations within Washington State but near the Washington–Idaho border, regional kriging is based solely on PM10 concentrations in the “Washington/Oregon, Northern California” region. It is not based on PM10 concentrations measured immediately across the border in Idaho, despite the real possibility that they would have the largest weights in national-scale kriging estimation. For all these reasons, we recommend national-scale kriging.
Considering the number of study participants and the length of study period (1994–2003) for the Environmental Epidemiology of Arrhythmogenesis in WHI study, development of an automated procedure enabling large-scale daily krigings and semivariogram cross-validations was critical. In this study, we decided to use ArcView for predicting individuals’ PM exposure concentrations because of the flexibility it offers for automation. Because ArcView GIS relies on either the weighted least-squares method or visual adjustment to create semivariograms, we did not compare the relative performance of semivariograms generated using alternative methods such as maximum likelihood and restricted maximum likelihood. For generating semivariograms, we compared only three popular spatial models (spherical, exponential, and Gaussian). Our results, however, do not invalidate alternative spatial models (e.g., power). In the end, we selected the spherical model for our study because it is the most studied model, and its assumption pertaining to the spatial correlation of data is probably closest to our pollutant data. Furthermore, the spherical model seemed to perform as well as or slightly better than the remaining models in terms of cross-validation parameters.
We chose ordinary kriging instead of universal or simple kriging for several reasons. First, the assumption for simple kriging of a known mean concentration on any given day across space is not practical for our data. Although it may seem more appropriate because of the “varying mean” concentration across the contiguous U.S. assumption, universal kriging requires a predetermined set of “exploratory variables” to explain the varying means. The candidates, many of which are spatial variables, include emissions, land use, population, road network distribution, altitude, rainfall, latitude, climatology, and other quality data. Denby et al. (2005) recently recommended a method that uses measured concentration data in combination with some “exploratory variables” as suggested above. However, their approach may not be feasible for a national-scale study such as ours, because little guiding information is available as to how to identify a set of widely acceptable variables that can be applied to the entire nation. Moreover, even if we could identify a set of exploratory variables, we do not know the forms or shapes of their independent and joint relations to the air pollution measures. Further studies that involve large-scale national data using universal kriging are still needed. In this study, we empirically tested whether the non-constant mean assumption for universal kriging was needed; we performed five regional ordinary krigings so that different parts of the country would assume a different mean PM concentration. Our data suggested that regional and national ordinary kriging performed similarly. Therefore, our data indirectly validated and supported the use of national ordinary kriging.
Although the primary objective of our study is to assess the short-term relationship between PM and cardiac responses, the proposed kriging method also enables us to calculate the long-term cumulative exposure of an individual by taking into account the change of his or her residences over time, because the WHI study recorded the residential location history over 10 years. Nevertheless, from the environmental perspective, an inherited limitation of the kriging-based approach is that the estimations of the PM concentrations will provide only surrogates, or the best guesses, of the true exposure levels at the locations of interest. Thus, the accuracy of the estimations depends highly on the quality of the measured data and their spatial correlation. Even if the estimations were made with a high level of confidence, they cannot be directly interpreted as the true individual-level exposures. However, to correlate individual level cardiac responses with a surrogate of location-specific exposure, our approach represents one of the best available methods for a large-scale population-based study.
In summary, our investigation of GIS approaches for estimating daily mean geocoded location-specific air pollutant concentrations and their SEs supports the use of a spherical model to perform lognormal ordinary kriging on a national scale. Our findings also support the use of default-generated semivariograms (estimated using the weighted least-squares method) without visual adjustment. We developed a semiautomated program to access and execute ArcView to implement these approaches for large-scale daily kriging estimations and semivariogram cross-validations. Detailed information about this program can be obtained on request.

kriging
cross-validation
geographic information systems
population-based studies
particulate air pollution


Monogenic diabetes in children and young adults: Challenges for researcher, clinician and patient

Monogenic diabetes results from one or more mutations in a single gene which might hence be rare but has great impact leading to diabetes at a very young age. It has resulted in great challenges for researchers elucidating the aetiology of diabetes and related features in other organ systems, for clinicians specifying a diagnosis that leads to improved genetic counselling, predicting of clinical course and changes in treatment, and for patients to altered treatment that has lead to coming off insulin and injections with no alternative (Glucokinase mutations), insulin injections being replaced by tablets (e.g. low dose in HNFα or high dose in potassium channel defects -Kir6.2 and SUR1) or with tablets in addition to insulin (e.g. metformin in insulin resistant syndromes). Genetic testing requires guidance to test for what gene especially given limited resources. Monogenic diabetes should be considered in any diabetic patient who has features inconsistent with their current diagnosis (unspecified neonatal diabetes, type 1 or type 2 diabetes) and clinical features of a specific subtype of monogenic diabetes (neonatal diabetes, familial diabetes, mild hyperglycaemia, syndromes). Guidance is given by clinical and physiological features in patient and family and the likelihood of the proposed mutation altering clinical care. In this article, I aimed to provide insight in the genes and mutations involved in insulin synthesis, secretion, and resistance, and to provide guidance for genetic testing by showing the clinical and physiological features and tests for each specified diagnosis as well as the opportunities for treatment.
Introduction
Monogenic diabetes is diabetes that results from one or more mutations in a single gene. The mutation might have arisen de novo and hence be a spontaneous case or might be dominantly or recessively inherited. Molecular genetic testing specifies a diagnosis in 1–2% [1]. The mechanism can be easily understood from the underlying pathophysiology. In children, almost all monogenic diabetes results from mutations in genes that regulate β-cell function and infrequently from mutations resulting in very severe insulin resistance [2]. This article discusses the pathophysiology and clinical manifestations used to select patients eligible to genetic testing, and demonstrates the importance in the treatment of monogenic diabetes.
Why diagnose monogenic diabetes?
Many patients with genetically proven monogenic diabetes were initially incorrectly diagnosed. They received a diagnosis of neonatal diabetes or diabetes diagnosed before the age of 6 months that was not further specified [3] or diagnosed as type 1 or type 2 diabetes [3–5]. It is important to correctly diagnose monogenic diabetes because not only does it help to elucidate the aetiology of the patient’s diabetes and explain other associated clinical features, it can also predict the clinical course of the patient and guide the most appropriate treatment. For instance, patients might not need any treatment or might be able to switch from insulin injections to tablets such as sulfonylurea [6, 7]. Finally a diagnosis has implications for other family members often correcting their diagnosis, prognosis and treatment as well as allowing appropriate genetic counselling.
Normal insulin release and normal insulin sensitivity
The β-cells form the core of the islets of Langerhans and are responsible for the synthesis and secretion of insulin and C-peptide (Fig. 1, upper part). Insulin’s main function, stimulating glucose uptake in peripheral tissue, is regulated by insulin binding to insulin receptors on the cell membrane of peripheral cells such as muscle cells. This binding initiates an intracellular signalling cascade that leads to an increase in the glucose influx (Fig. 2).
Fig. 1Pancreatic β-cell and the genes involved in monogenic diabetes. Mutations in different genes result in different phenotypes (Tables 1, 2, 3, and 4). Also, different mutations in the same gene might lead to different phenotypes as shown in the spectrum of phenotypes in for instance Kir6.2.The upper part shows the physiological situation from insulin synthesis to packaging and from glucose sensing to insulin secretion. From centre to right: Insulin synthesis and packaging: Insulin (Ins) is synthesized in the nucleus regulated by transcription factors and after translation in the endoplasmatic reticulum (ER) and Golgi apparatus (Golgi) stored in granules. From left to right down: Glucose sensing and insulin secretion: Glucose enters the β-cell by passive diffusion facilitated by the glucose-transporter-2 (GLUT2). It is phosphorylated by the enzyme glucokinase (GCK) to glucose-6-phosphate (G6P) and metabolised to ATP via glycolysis or even further via the Krebs cycle in the mitochondria (Mito). ATP closes the KATP channel, preventing K+ efflux, depolarising the cell membrane. Depolarisation opens voltage dependent calcium channels (VDCC) allowing calcium influx. The rise in intracellular calcium (Ca2+) helps the insulin granules to fuse with the cell membrane resulting in insulin secretion.The lower part shows the pathological situation due to mutations in the genes involved in monogenic diabetes. The proteins encoded by the genes involved are given in bold type followed by the clinical presentation if mutated (in brackets and italics). From centre to right: Insulin synthesis is mainly influenced by nuclear transcription factors that may also be involved in pancreatic development and hence mutations may result in pancreatic atrophy (PTF1α, HNF1β) or agenesis (IPF1) rather than reduced insulin synthesis per se as in TNDM (ZAC) or MODY (HNF1α, HNF4α, NEUROD1). Mutations in genes that are involved in packaging of the insulin into granules in the ER and Golgi apparatus result in TNDM (HYMAI), β-cell destruction and PNDM (EIF2AK3 in Wolcott Rallison Syndrome—WCRS), diabetes at a mean age of six as part of the Diabetes Insipidus, Diabetes Mellitus, Optic Atrophy and Deafness syndrome (DIDMOAD) alternatively called Wolfram syndrome (WRS) (WSF1), or in diabetes as part of the Thiamine Responsive Megaloblastic Anaemia Syndrome (TRMAS) alternatively called Roger’s syndrome. Mutations in T-lymphocytes may also lead to β-cell destruction and PNDM as seen with mutations in FOXP3 as part of the Immunodysregulation, Polyendocrinopathy, Enteropathy, X-linked (IPEX) syndrome. From left to right down: Glucose sensing is reduced by mutations in GLUT2- leading to TNDM as part of the Fanconi-Bickel Syndrome, GCK- resulting in MODY2 and mitochondrial DNA-interfering with oxidative phosphorylation. These latter three mutations reduce ATP and increase ADP leading to a decreased ATP/Mg-adenosine diphosphate (ADP) ratio that activates the KATP channel to remain open. Activating mutations in the KATP channel itself (Kir6.2/SUR1) reduce sensitivity to ATP and hence also favour the open state of the channel. The subsequent efflux of potassium prevents depolarisation of the cell-membrane and hence prevents insulin secretionFig. 2Peripheral cell (e.g. muscle cell) showing the impact of diverse mutations that result in insulin resistance. In the physiological situation, insulin binds to the insulin receptor resulting in phosphorylation (P) of a tyrosine (Y) residue of the insulin receptor substrate-1 (IRS1). This phosphorylation activates phosphatidylinositol 3-kinase (PI3K) resulting in glucose transporter 4 (GLUT4) being translocated to the cell membrane which in turn leads to glucose influx. In the pathological situation, mutations in the insulin receptor interfere with insulin receptor synthesis, posttranslational processing and intracellular-transport of the receptor to the cell membrane or lead to reduced binding of insulin, reduced activation or increased degradation of the receptor. The result is no phosphorylation of Y and hence eventually no glucose uptake. This situation occurs in Type A severe insulin resistance, Rabson-Mendenhall and Leprechaunism. In the presence of high levels of triglycerides (TG) and hence free fatty acids (FFA) a serine (S) residue is phosphorylated preventing phosphorylation of Y and hence eventual glucose uptake. This situation occurs in congenital generalised lipoatrophy and familial partial lipodystrophy. In all these pathological situations, higher levels of insulin are needed for glucose uptake and hence lead to reduced insulin sensitivity and insulin resistance
Abnormal insulin release and abnormal insulin sensitivity
Most monogenic diabetes in children results from mutations in genes causing β-cell loss or β-cell dysfunction and hence affect insulin synthesis, packaging, glucose sensing or insulin secretion (Fig. 1, lower part) [3, 8]. Mutations of pancreatic transcription factors may result in reduced pancreatic growth, (sub)total pancreatic agenesis or reduced insulin synthesis per se and may present as neonatal diabetes (ND), maturity onset diabetes of the young (MODY), or diabetes as part of a syndrome (Fig. 1, lower part centre). Other mutations that affect insulin synthesis are not expressed in the pancreatic β cell but in the CD4+ CD25+ regulatory T lymphocytes, where overactivation results in auto-immunity against β-cells typically causing diabetes in the first 3 months of life. Mutations affecting translation, cleavage and packaging of insulin may result in reduced β-cell number or reduced number of granules and lower concentrations of insulin in these granules (Fig. 1 lower part right).
Mutations that affect insulin secretion involve genes that regulate glucose sensing and affect beta-cell function rather than its development or destruction. They include functional mutations in mitochondrial DNA (Fig. 1, lower part left). All these mutations reduce sensitivity to glucose and its metabolism favouring the open state of the potassium channels prevent depolarisation and hence insulin secretion.
In children, monogenic diabetes infrequently occurs from mutations resulting in very severe insulin resistance (Fig. 2) [2]. They are mainly caused by mutations in the insulin receptor gene, that affect its synthesis and posttranslational processing, increase receptor degradation, reduce binding of insulin or receptor activation [9]. These give rise to type A severe insulin resistance, Rabson-Mendenhall syndrome or Leprechaunism [2, 9–11]. Alternatively, insulin resistance may be the result of hypertriglyceridaemia associated with congenital generalised lipoatrophy or familial partial lipodystrophia [12, 13].
When to consider a diagnosis of monogenic diabetes?
Given the limited resources available it is vital that genetic tests are used in situations where they are likely to be positive and will alter clinical care. This will involve careful clinical selection and physiological tests like C peptide and autoantibody measurement as well as examination of other family members before doing molecular genetic tests. Monogenic diabetes should be considered in any diabetic patient who has features inconsistent with their current diagnosis and clinical features of specific subtypes of monogenic diabetes.
No specified diagnosis or features inconsistent with current diagnosis
A diagnosis of diabetes might have been made without further specifying the cause as in neonatal diabetes or diabetes diagnosed below the age of 6 months. Also, the majority of patients with genetically proven monogenic diabetes are initially incorrectly diagnosed as Type 1 or Type 2 diabetes [5].
No specified diagnosis of neonatal diabetes or of diabetes diagnosed below the age of 6 months
Neonatal diabetes was defined as insulin requiring diabetes diagnosed within the first 3 months of life. It is an area that has rapidly transitioned from a clinical to a molecular genetic classification [3, 14]. There is good evidence that diabetes diagnosed in the first 6 months of life is not type 1 diabetes because neither autoantibodies typical for diabetes nor an excess of high type 1 HLA susceptibility are found in these patients [15]. Diabetes diagnosed before the age of 6 months irrespective of current age should always lead to further molecular genetic testing. Also because many years after diagnosis, patient and doctor will find difficulty in recalling the exact age of diagnosis and only be aware that it was ‘at very young age’. Features that help differentiate between the different subtypes and hence guide genetic testing are further described in the section below and in Table 1.
Table 1Features of diabetes diagnosed before 6 months of age in addition to undetectable to low C-peptideProtein (Chromosome/gene; Syndrome)Clinical pictureNumber of cases describedMedian birth weight ingrams SDS (standard deviation score)Median age at diagnosis in weeks (range)Family history reflected by inheritanceOther clinical featuresOther testsTreatment(% in consanguineous or isolated populations)Pancreatic appearance (present/size)• ZAC/HYMAI (6q24 imprinting defect)TNDM±150 (rare)2,100 (−2.94)0.5 (0–4)- Macroglossia (23%)NormalInsulin/pump > relapse: diet > insulin• Kir6.2 (KCNJ11)TNDM10% PNDM90%±100 (rare)2,580 (−1.73)6 (0–260)- Spontaneous- DKA (30%)NormalHigh dose sulfonylurea- Dominant(10%)- Developmentaldelay 20%)- Epilepsy (6%)• PTF1A(10p13-12)PNDM3 (100%)1,390 (−3.8)Recessive- Severeneurological dysfunctionAtrophyInsulin/pump- Cerebellar hypoplasia• IPF1 (13q12.1)PNDM2 (50%)2,140 (−2.97)- RecessiveNo pancreasInsulin/pump- Parents may have early onset diabetes as heterozygotes• HNF1β (179)TNDMRare1,900 (−3.21)- Dominant (60%)- Renal development disordersAtrophyInsulin/pump- Spotaneous• EIF2AK3(2p;Wolcott-Rallison Syndrome)PNDM30 (90%)13 (6–65)- Recessive- Epiphyseal dysplasia (90%)Exocrine dysfunctionInsulin/pump- Developmental delay (80%)- Acute liver failure (75%)- Osteopenia (50%)- Hypothyroidism (25%)• FOXP3 (Xp11.23; IPEX Syndrome)PNDM14 (rare)2,860 (−1.2)6 (0–30)X-linked Hence only boys affected- Chronic diarrhoea with villous atrophy (95%)Insulin/pump- Pancreatic and thyroid autoantibodies (75%)- Eczena (50%)- Anaemia (30%)- Thyroiditis (20%)- Often die in first year• GLUT2 (3q; Fanconi Bickel Syndrome)TNDMRecessive- Impaired utilisation of glucose and galactoseInsulin/pump- Hepatorenal glycogen accumulation- Proximal renal tubular dysfunction > glucosuria• Glucokinase (GCK11 homozygote)PNDM6 (85%)1,720 (−2.75)- RecessiveNormalInsulin/pump- Parents have fasting hyperglycaemia as heterozygotes
Clinical features that are unusual for type 1 diabetes
Type 1 diabetes shares clinical features with certain types of monogenic diabetes, such as young age of diagnosis, normal body weight and insulinopaenia [5] so that a monogenic diagnosis can be overlooked. Features in children initially thought to have type 1 diabetes but that should suggest a possible diagnosis of monogenic diabetes are shown below. None of these are absolute and should be considered collectively rather than in isolation [4]. The approximate percentage of patients with type 1 diabetes is given in brackets.
Age of diagnosis below 6 months after birth (<1%) [15]Family history with a parent affected (2–4%) [16]Endogenous insulin production after 3 years of diabetes (the honeymoon phase), indicated by detectable C-peptide (>200 nmol/l) in response to raised glucose (>8 mmol/l) (1–5%).Absence of islet autoantibodies, especially when measured at diagnosis (3–30%) [17, 18]. The great variation in antibody prevalence in series probably represents differences in assays and means it is hard to apply published series directly into clinical practice. Absent antibodies should lead to other investigation/consideration rather than leading directly to genetic tests.
Clinical features that are unusual for type 2 diabetes
Type 2 diabetes in children and young adolescents might share features which meet former classification criteria for MODY such as diagnosed <25 years, autosomal dominant inheritance and non-insulin dependency [19–22]. Patients might have a rather low body mass index and have other features that are unusual for type 2 diabetes. These should raise suspicion for the current diagnosis to be correct as chances for these features occurring in type 2 diabetic children or adolescents are low as shown here (percentage shown in brackets).
No evidence of insulin resistance with fasting C-peptide within the normal range (0–20%) [19–22].Ethnic background from a low prevalence type 2 diabetes race e.g. European Caucasian (0–45%) [19–22]Acanthosis nigricans not detected (10%) [19]Not markedly obese or diabetic family members who are normal weight (20%) [22].
Clinical features of specific subtypes of monogenic diabetes and their treatment
Typical clinical presentations in children when a diagnosis of monogenic diabetes should be considered can mainly be classified into four categories that are further discussed below:
Neonatal diabetes and diabetes diagnosed within the first 6 months of life (Table 1)Familial diabetes with an affected parent (autosomal dominant) (Table 2 upperpart)Mild (5.5–8.5 mmol/l) fasting hyperglycaemia especially if young or familial (Table 2 lower part).Features in other tissues as part of genetic syndromes associated with diabetes (Tables 3 and 4)
Neonatal diabetes and diabetes diagnosed before the age of 6 months irrespective of current age
Diabetes diagnosed before the age of 6 months is most likely to have a genetic cause, and further clinical aspects should be investigated to guide genetic testing. Clinically two subgroups were recognised: transient neonatal diabetes mellitus (TNDM) that resolved at a median of 12 weeks and then did not require any treatment although as many as 50% of cases relapsed during the paediatric age range [23, 24]. In contrast permanent neonatal diabetes mellitus (PNDM) required lifelong insulin injections from diagnosis onwards. The majority of patients with TNDM have an imprinting abnormality of the transcription factor ZAC gene and HYMAI gene encoding an untranslated RNA chromosome 6q (Table 1) [14, 23]. Apart from macroglossia seen in 23% there are no non-pancreatic features [23]. The second commonest cause of mutations in patients with diabetes diagnosed before the age of 6 months of life are mutations in the KCNJ11 gene encoding the Kir6.2 subunit of the KATP channel and that can result in either TNDM (10%) or PNDM (90%) [3, 25–27]. Despite being a heterozygous mutation most have no family history because 90% of cases are spontaneous mutations. Most patients have isolated diabetes although neurological features are seen in 20% of patients. The most severe is the Developmental delay early onset generalised Epilepsy and Neonatal Diabetes (DEND) syndrome [25]. More common is the intermediate DEND syndrome where patients have less severe developmental delay and do not have early onset generalised epilepsy and that is often associated with the V59M mutation [3, 26]. Recently, activating mutations in the ABCC8 gene encoding the SUR1 subunit of the KATP channel were found to be a similar cause of neonatal diabetes [28]. While the majority of cases in PNDM are the result of mutations in the KCNJ11 gene, in a small minority of patients, many different genetic mutations have been described [3]. If both parents are glucose intolerant, homozygous or compound heterozygous mutations in glucokinase are most frequent [29, 30]. Features that help differentiate between TNDM, PNDM and the different subtypes, guide which gene to test for and are described in Table 1.
Concerning treatment, imprinting abnormalities of the 6q24 locus show initially very high glucose values (range 12–57 mmol/l) and so insulin is used initially although the dose can rapidly be reduced. Once patients have relapsed patients should remain under annual follow up due to the risk of diabetes relapsing. Relapse patients are not insulin dependent and can be treated with diet initially although subsequently often need insulin [14]. The long-term response to oral treatment such as sulfonylurea or metformin is uncertain.
Patients with Kir6.2 mutations have all the clinical features of insulin dependency as 30% present with ketoacidosis and they usually do not have detectable C peptide and so were treated with insulin [26]. It has recently been shown that these patients cannot only be successfully treated with oral sulfonylureas but can also get better glycaemic control without an increase in hypoglycaemia and with neurological features improving as well [70]. The doses needed are high when calculated on a per kg body weight basis compared to adults, with patients typically needing 0.5 mg glibenclamide/kg/day although some may need as much as 1 mg/kg/day [31–36, 70]. With time many patients have been able to reduce their doses of sulfonylurea while maintaining excellent glycaemic control [7, 70]. Similar results have recently been found for patients with activating SUR1 mutations [37]. All other causes need to be treated with insulin. Some paediatricians find these patients are best managed on subcutaneous insulin pumps due to the fluctuations in glucose levels. In patients with pancreatic aplasia exocrine pancreatic supplements will additionally be required.
Familial diabetes with an affected parent
Children and young adults with a strong family history of diabetes
Genetic causes of a family history of diabetes are autosomal dominant and non-insulin dependent. A diagnosis of Maturity Onset Diabetes of the Young (MODY) should be considered whenever a parent has diabetes even if they are thought to have type 1 or type 2 diabetes (Table 2). MODY comes in two major subgroups: (1) the result of transcription factor mutations (IPF1-MODY4, HNF1β-MODY5, HNF1α-MODY3, HNF4α-MODY1, NeuroD1-MODY6 or CEL-MODY7) described in this section and (2) mutations reducing glucose sensing (glucokinase mutations-MODY2) in which a family history might be less evident because it only presents as mild hyperglycaemia as described in the following section. MODY due to transcription factor mutations often present in adolescence/young adulthood, show progressive hyperglycaemia and frequently lead to complications. Of the transcription factor mutations, MODY3—due to HNF1α mutations—is the commonest form [38]. The clinical characteristics of patients with HNF1α mutations are:
Young onset diabetes that shows characteristics of not-being insulin dependent e.g., do not develop keto-acidosis in the absence of insulin, achieve good glycaemic control on a small dose of insulin. Detectable C-peptide is measured when on insulin with glucose >8 mmol/l after 3 years of diabetes (the honeymoon period).Family history of diabetes. This might be insulin treated and considered to be type 1 diabetes. This would typically be diagnosed at their 20s, 30s or 40s. There may also be an affected grandparent although often these are diagnosed after 45 years.Glucosuria at relatively normal blood glucose levels are often seen as these patients have a low renal threshold [39].Oral glucose tolerance tests in early stages tend to show a very large glucose increment usually >5 mmol/l [39]. Some patients might have a normal fasting value while still rise into the diabetic range at 2 h.Marked sensitivity to sulfonylurea resulting in hypoglycaemia despite poor glycaemic control before starting sulfonylurea [40,41].Table 2Familial diabetes diagnosed, or undiagnosed due to mild hyperglycaemiaGene/proteinClinical pictureNumber of cases describedMedian age at diagnosis in weeks (range)Family history reflected by inheritanceOther clinical featuresOther testsTreatmentGlucose at presentation in mmol/l Median (range)OGTTFamilial diabetes diagnosedHNF-1αMODY319714 (4–18)DominantHyperglycaemia is rapidly progressive with age17 (11–26)Large increment (0 h–2 h usually >5 mmol/l)Diet > low dose of sulfonylureaLow renal threshold > glucosuriaSensitive to sulfonylureaHNF-4αMODY12217 (5–18)DominantHyperglycaemia is rapidly progressive with age15 (9–20)Large increment (0 h–2 h usually >5 mmol/l)Low dose of sulfonylureaNormal renal thresholdSensitive to sulfonylureaReduced levels of apoAIII, apoCIII, and triglyceridesOther unusual causes: IPF1 (MODY4), NeuroD1 (MODY6), CEL (MODY7)Familial diabetes undiagnosed due to mild fasting hyperglycaemiaGlucokinase (GCK, heterozygous)MODY215210 (0–18)DominantHyperglycaemia is mild (fasting 5.5–8 mmol/l)11 (5.5–16)Small increment (0 h–2 h usually <3.5 mmol/l)No treatment(The mild hyperglycaemia might not have been diagnosed in relatives/parents)Hyperglycaemia is only slowly progressive with age> usually diagnosis is by incidental findingNormal renal threshold
Patients with HNF1α gene mutations can initially be treated through diet although they will have marked postprandial hyperglycaemia after high carbohydrate food as the β-cell defect results in insufficient increase in insulin secretion with hyperglycaemia [42]. Most patients will need pharmacological treatment as they show progressive deterioration in glycaemic control throughout life and are at risk of considerable micro-vascular and macro-vascular complications [43]. The first treatment to be used in children who are not controlled on insulin should be low dose sulfonylureas which results in a 4-fold greater lowering of glucose than metformin [41]. These patients are extremely sensitive to sulfonylurea and as long as they do not have problems with hypoglycaemia can be maintained on these for many decades [40]. Glycaemic control in sulfonylureas is often better than that achieved on insulin especially in children and young adults [44]. The dose of sulfonylureas should initially be low (1/4 of the normal starting dose in adults) to avoid hypoglycaemia. If there is hypoglycaemia despite dose titration of a once or twice daily sulfonylurea preparation such as Gliclazide, a slow release preparation or mealtime doses with short-acting agents like nateglinidine may be considered [45].
Mutations in the hepatocyte nuclear factor 4α (HNF4α)-gene resulting in MODY1 are considerably less common, have similar characteristics but might be diagnosed later and patients have no low renal threshold [46]. They also lead to reduced levels of triglycerides and the apolipoproteins apoAIII and apoCIII [47]. Diagnosis should be considered when HNF1α tests negative while the clinical features are strongly suggestive for HNF1α [46]. Patients with HNF4 α mutations are usually sensitive to sulfonylurea [48].
The handful of families with autosomal dominant non-insulin dependent diabetes that have further been described include mutations in IPF1 (MODY4) [49], NeuroD1 (MODY6) [50,51], and recently the carboxyl ester lipase (CEL) gene (MODY7) [52], but these are so unusual they do not need to be tested for in children with diabetes except in a research setting or when there are additional phenotypes such as pancreatic exocrine dysfunction [52]. IPF has been described under neonatal diabetes, NeuroD1 encodes a transcription factor that binds to a critical Ebox motif on the insulin promoter and plays a role in both pancreatic and neuron development.
Mild (5.5–8.5 mmol/l) fasting hyperglycaemia especially if young or familial
Raised fasting blood glucose in the range of 5.5 to 8.5 mmol/l is unusual in children and young adults. This always raises concern that they may be about to develop type 1 diabetes or have type 2 diabetes. However a considerable proportion of these patients with persistent mild fasting hyperglycaemia will have a heterozygous mutation in the glucokinase gene. The phenotype associated with glucokinase mutations is remarkably similar for all mutations with the following features suggesting a diagnosis.
The fasting hyperglycaemia is persistent and stable over a period of months or years [39]HbA1C is typically just below or just above the upper limit of normal (5.5 to 5.7%)In an oral glucose tolerance test the increment (2 h glucose-fasting glucose) is small (typically <3.5 mmol/l) although because of the variability of the oral glucose tolerance test this should not be considered an absolute criteria [39].Parents may have ‘type 2 diabetes’ or may not be diabetic. On testing one parent will have a mildly raised fasting blood glucose, in the range of 5.5–8.5 mmol/l, as this is an autosomal dominant condition [39]. Testing of fasting glucose in apparently unaffected parents is important when considering a diagnosis of a glucokinase mutation.
The fasting hyperglycaemia does not deteriorate significantly and the glucose is regulated at the higher set point [39]. As this is rarely associated with any microvascular or macrovascular complications even when no treatment is given throughout life and as there is very little if any response to either oral hypoglycaemic agents or insulin because the set point remains the same, these patients do not need treating in the paediatric age range [53].
Genetic syndromes associated with diabetes
When diabetes in a child is associated with other multi-system disease the possibility of a monogenic syndrome that explains all features should be considered. The online Mendelian inheritance in Man (OMIM) website (access through the NCBI website http://www.ncbi.nlm.nih.gov/entrez/query.fcgi) can help with clinical features and to know if the gene has been defined and hence molecular genetic testing is available. For described and previously undescribed syndromes see the ISPAD rare diabetes collection (contact through link on the ISPAD website or through http://www.diabetesgenes.org). The most common genetic syndromes that include diabetes are described below and in Tables 3 (insulin synthesis/secretion) and 4 (insulin resistance).
Table 3Syndromic features in addition to the diabetes: insulin synthesis/secretionGene/proteinClinical pictureNumber of cases describedMedian age at diagnosis in weeks (range)Family history reflected by inheritanceOther clinical featuresTreatmentHNF1βRarely isolated PNDM or MODY 5- Renal developmental disorders, especially renal cysts and dysplasiaInsulin (+possibly treat exocrine deficiency?)HNF1βRenal cysts and diabetes syndrome (RCAD)- Uterine and genitalia developmental anomalies- Hyperuricaemia, gout- Abnormal liver function testsWSF1Diabetes insipidus, diabetes mellitus, optic atrophy, deafness (DIDMOAD) syndrome/Wolfram syndrome (90% have mutations)Especially where consanguineous marriages are frequent6 years(Most <16 years)Dominant- Diabetes insipidus- Optic atrophy - Bilateral sensorineural deafness- Dilated renal tracts- Truncal ataxia- Protean neurological signs 75% has the complete phenotype, increasing with increasing ageInsulinSLC19A2(Thiaminetransporter protein)Thiamine responsiveMegaloblastic anaemia (TRMA) syndrome Roger’s syndromeRareRecessive- Thiamine responsive megaloblastic anaemia- Sensorineural deafnessThiamine > insulin   tRNA(leu(UUR)) gene (3243 A to G; tRNA)- Maternally inherited diabetes (MID)- Mitochondrial myopathy, encephalopathy, lactic acidosis, stroke-like syndrome (MELAS)- Sensorineural deafness- Short stature- Subclinical exocrine deficiency- HeteroplasmyInsulinTable 4Syndromic features in addition to the diabetes: insulin resistanceProteinClinical pictureMedian age at diagnosis in weeks (range)Family history reflected by inheritanceOther clinical featuresOther features/testsTreatmentAcanthosis nigricansInsulin levelsAndrogen excess and hypertrichosisInsulin receptorType AAdolescenceRecessive (usually)Insulin resistance in absence of obesityYes—marked↑↑↑↑↑↑/PCO(Metformin/glitazones) > insulin/pumpInsulin receptorRabson-MendenhallCongenitalRecessive (usually)- Abnormal dentitionYes—marked↑↑↑↑↑/PCO(Metformin/glitazones) > insulin/pump- Extreme growth retardationInsulin receptorLeprechaunism (Donahue syndrome)CongenitalRecessive (usually)- Abnormal faciesYes—marked↑↑↑↑↑↑/PCO(Metformin/glitazones) > insulin/pump- SGA and growth retardation- Large genitalia- Rarely survive infancySeipin&AGPAT2Total lipodystrophyAdolescence or congenitalRecessive- Total loss of subcutaneous fatYes—may be marked↑↑↑↑↑/PCO+/−Recombinant /insulinLamin AC&PPARγPartial lipodystrophyDominant- Partial loss of subcutaneous fatMetformin > insulin
MODY-5 due to an HNF-1β mutation (renal cysts and diabetes syndrome
The transcription factor hepatocyte nuclear factor 1 β is expressed in primitive pancreatic duct cells involved in early endocrine cell differentiation incidentally leading to PNDM, but mainly leading to MODY5 with pancreatic atrophy as a consistent finding when assessed by abdominal computed tomography [54, 55]. Patients with HNF-1β mutations rarely present with isolated diabetes [56]. Renal developmental disorders especially renal cysts and renal dysplasia are present in almost all patients with mutations or gene deletions [57]. These may be diagnosed in utero and precede the diagnosis of diabetes. A diagnosis of HNF-1β should be considered in any child with diabetes who also has non-diabetic renal disease. Other features are presented in Table 3. Patients with HNF1-β mutations, unlike patients with HNF-1α mutations, are not sensitive to sulfonylurea and hence usually require insulin treatment [58]. Pancreatic size is reduced reflecting a reduction in both the endocrine and exocrine pancreas and sub-clinical exocrine deficiency is present in most patients [57] but it is uncertain if this should be treated if it is asymptomatic.
Wolfram or DIDMOAD syndrome (diabetes insipidus, diabetes mellitus, optic atrophy and deafness)
This autosomal recessive syndrome shows mutations in the gene for Wolfram syndrome (WFS1) in at least 90% of patients [59–61], and the association of diabetes with progressive optic atrophy below 16 years of age is diagnostic [62]. Other features are described in Table 3 with the complete phenotype seen in 75% of patients with increasing prevalence with age. The order of appearance of the neurological features may vary, even within families. Patients with Wolfram syndrome die at a median age of 30 years [62]. The diabetes is non-autoimmune and insulin deficient and presents at a mean age of 6 years [62]. Patients require insulin treatment from the time of diagnosis but autoantibodies are not present [62].
Roger’s or TRMA syndrome (thiamine responsive megaloblastic anaemia)
The diabetes in this rare recessive genetic syndrome, is insulin deficient in nature and both the diabetes and the anaemia are responsive to thiamine. However, all seem to develop an insulin requirement in the long term [63]. Further details are described in Table 3.
Mitochondrial diabetes
Maternal transmission of mutated or deleted mitochondrial DNA (mtDNA) can result in maternally inherited diabetes [64] although they do not usually occur in the paediatric age range. Patients suffer from sensorineural deafness and short stature. In family members there might be some overlap between MIDD and MELAS syndrome [65, 66]. The heteroplasmy in proportions of mitochondrial DNA affected lead to a variety of phenotypes. The diabetes is characterised by progressive non-autoimmune β-cell failure, usually manifests outside the paediatric age range, and may progress to needing insulin treatment rapidly. Other features are described in Table 3.
Insulin resistance syndromes
The key features of all insulin resistance syndromes are acanthosis nigricans, massively raised insulin concentrations, and androgen excess while obesity is absent (Table 4) [2]. The more severe the insulin resistance and the earlier the onset, the more likely is diabetes. The mechanism of the main insulin resistance syndromes, Type A, Rabson-Mendenhall, Leprechaunism and Lipodystrophy, is shown in Fig. 2 while the key clinical features of the main insulin syndromes, are presented in Table 4 [2]. Treatment of severe insulin resistance is very difficult and most patients with diabetes have poor glycaemic control and frequently develop long-term complications [2]. Treatment approaches include the use of the insulin sensitisers metformin and glitazones, but effectiveness is limited when the insulin resistance is very severe. Insulin is the main stay of treatment and U500 insulin and insulin pumps are usually required [2]. In partial lipodystrophy metformin may have benefit and insulin is not required in the early stages [67]. In total lipodystrophy the response of diabetes to recombinant lipodystrophy [68] can be dramatic but is only available on a research basis.
Testing for a molecular monogenic diagnosis
How to test for monogenic diabetes?
While in type 1 and type 2 diabetes there is no single diagnostic test, this is not the case in monogenic diabetes where in >80% of cases a molecular genetic diagnosis can be made by DNA testing. These are offered by many laboratories but should preferably be performed in a laboratory that is experienced and specialised in such diagnosis irrespective whether this requires sending samples abroad (http://www.diabetesgenes.org). Some recently described monogenic genes, such as Kir6.2 in patients in whom diabetes was diagnosed before the age of 6 months, can be screened for with no charge (http://www.diabetesgenes.org). Other tests are more expensive (up to 500 Euro or 635 USD) but make future patient care cheaper and hence testing would be cost-effective. For example, in a Swiss centre yearly costs for type 1 diabetes was approximately 2050 Euro (2603 USD) per type 1 patient compared to 410 Euro (521 USD) per MODY2 patient [69]. Approval from the patient’s insurance company should be sought prior to sending DNA when applicable. Future analyses will show cost-effectiveness for the different subtypes and prove earlier diagnosis to be more beneficial.
What if a monogenic diagnosis cannot be made?
Occasionally molecular genetic test results are negative, despite unusual clinical features or typical features for a certain monogenic subtype. The certainty of such a negative result increases if a specialised centre performed testing. Even then, some cases remain unsolved. These can be referred to the ISPAD rare cases registry (see website or contact a.t.hattersley@exeter.ac.uk) to allow pattern finding and closer investigation by experts if a novel idea evolves, hence increasing chances for new and future insights, novel diagnoses and improved patient care.
Summary
Molecular genetic testing can define a diagnosis in 1–2% of all diabetic patients with monogenic diabetes. Advances in this field have led to the identification of the genes associated with many clinically identified subgroups of diabetes and explained clinical heterogeneity in conditions defined by age of diagnosis e.g. neonatal diabetes and MODY. Molecular genetic tests are now available to help define the diagnosis, and importantly alter prognosis and optimise treatment of children, young adults and their families with diabetes. As these tests are expensive genetic testing should be limited to those who on clinical grounds are likely to be positive. Considering testing for monogenic diabetes is hence a challenge and should be guided by unusual features of the current diagnosis, specific features concordant with monogenic subtypes and by the possibility of a change in treatment. This article discussed the pathophysiology and clinical manifestations used to select eligible patients and guide genetic testing, and demonstrates its importance in the treatment of monogenic diabetes.

monogenic diabetes
kir6.2
insulin resistance syndrome
neonatal diabetes
gck
mody