Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation

BACKGROUND

The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy.

METHODS

For this observational study, we conducted a combined analysis of individual-participant data from 19 high-income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23·1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU.

FINDINGS

For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2·69 (95% CI 2·43-2·97) when diagnosed at 30-39 years, 2·26 (2·08-2·45) at 40-49 years, 1·84 (1·72-1·97) at 50-59 years, 1·57 (1·47-1·67) at 60-69 years, and 1·39 (1·29-1·51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier when diagnosed aged 50 years than an individual without diabetes. Using EU death rates, the corresponding estimates were 13, 9, or 5 years earlier.

INTERPRETATION

Every decade of earlier diagnosis of diabetes was associated with about 3-4 years of lower life expectancy, highlighting the need to develop and implement interventions that prevent or delay the onset of diabetes and to intensify the treatment of risk factors among young adults diagnosed with diabetes.

FUNDING

British Heart Foundation, Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.

Well-differentiated gastroenteropancreatic G3 NET: findings from a large single centre cohort

Neuroendocrine neoplasms are known to have heterogeneous biological behavior. G3 neuroendocrine tumours (NET G3) are characterized by well-differentiated morphology and Ki67 > 20%. The prognosis of this disease is understood to be intermediate between NET G2 and neuroendocrine carcinoma (NEC). Clinical management of NET G3 is challenging due to limited data to inform treatment strategies. We describe clinical characteristics, treatment, and outcomes in a large single centre cohort of patients with gastroenteropancreatic NET G3. Data was reviewed from 26 cases managed at Queen Elizabeth Hospital, Birmingham, UK, from 2012 to 2019. Most commonly the site of the primary tumour was unknown and majority of cases with identifiable primaries originated in the GI tract. Majority of cases demonstrated somatostatin receptor avidity. Median Ki67 was 30%, and most cases had stage IV disease at diagnosis. Treatment options included surgery, somatostatin analogs (SSA), and chemotherapy with either platinum-based or temozolomide-based regimens. Estimated progression free survival was 4 months following initiation of SSA and 3 months following initiation of chemotherapy. Disease control was observed following treatment in 5/11 patients treated with chemotherapy. Estimated median survival was 19 months; estimated 1 year survival was 60% and estimated 2 year survival was 13%. NET G3 is a heterogeneous group of tumours and patients which commonly have advanced disease at presentation. Prognosis is typically poor, though select cases may respond to treatment with SSA and/or chemotherapy. Further study is needed to compare efficacy of different treatment strategies for this disease.

Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ∼2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids.

Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci

To identify genetic factors contributing to type 2 diabetes (T2D), we performed large-scale meta-analyses by using a custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) with ∼2000 candidate genes in 39 multiethnic population-based studies, case-control studies, and clinical trials totaling 17,418 cases and 70,298 controls. First, meta-analysis of 25 studies comprising 14,073 cases and 57,489 controls of European descent confirmed eight established T2D loci at genome-wide significance. In silico follow-up analysis of putative association signals found in independent genome-wide association studies (including 8,130 cases and 38,987 controls) performed by the DIAGRAM consortium identified a T2D locus at genome-wide significance (GATAD2A/CILP2/PBX4; p = 5.7 × 10(-9)) and two loci exceeding study-wide significance (SREBF1, and TH/INS; p < 2.4 × 10(-6)). Second, meta-analyses of 1,986 cases and 7,695 controls from eight African-American studies identified study-wide-significant (p = 2.4 × 10(-7)) variants in HMGA2 and replicated variants in TCF7L2 (p = 5.1 × 10(-15)). Third, conditional analysis revealed multiple known and novel independent signals within five T2D-associated genes in samples of European ancestry and within HMGA2 in African-American samples. Fourth, a multiethnic meta-analysis of all 39 studies identified T2D-associated variants in BCL2 (p = 2.1 × 10(-8)). Finally, a composite genetic score of SNPs from new and established T2D signals was significantly associated with increased risk of diabetes in African-American, Hispanic, and Asian populations. In summary, large-scale meta-analysis involving a dense gene-centric approach has uncovered additional loci and variants that contribute to T2D risk and suggests substantial overlap of T2D association signals across multiple ethnic groups.

The influence of surface energy on the self-cleaning of insect adhesive devices

The ability of insects to adhere to surfaces is facilitated by the use of adhesive organs found on the terminal leg segments. These adhesive pads are inherently ‘tacky’ and are expected to be subject to contamination by particulates, leading to loss of function. Here, we investigated the self-cleaning of ants and beetles by comparing the abilities of both hairy and smooth pad forms to self-clean on both high and low energy surfaces after being fouled with microspheres of two sizes and surface energies. We focused on the time taken to regain adhesive potential in unrestrained Hymenopterans (Polyrhachis dives and Myrmica scabrinodis) and Coccinellids (Harmonia axyridis and Adalia bipunctata) fouled with microspheres. We found that the reattainment of adhesion is influenced by particle type and size in Hymenopterans, with an interaction between the surface energy of the contaminating particle and substrate. In Coccinellids, reattainment of adhesion was only influenced by particle size and substrate properties. The adhesive organs of Coccinellids appear to possess superior self-cleaning abilities compared with those of Hymenopterans, although Hymenopterans exhibit better adhesion to both surface types.

Blood pressure loci identified with a gene-centric array

Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a bespoke gene-centric array to genotype an independent discovery sample of 25,118 individuals that combined hypertensive case-control and general population samples. We followed up four SNPs associated with BP at our p < 8.56 × 10(-7) study-specific significance threshold and six suggestively associated SNPs in a further 59,349 individuals. We identified and replicated a SNP at LSP1/TNNT3, a SNP at MTHFR-NPPB independent (r(2) = 0.33) of previous reports, and replicated SNPs at AGT and ATP2B1 reported previously. An analysis of combined discovery and follow-up data identified SNPs significantly associated with BP at p < 8.56 × 10(-7) at four further loci (NPR3, HFE, NOS3, and SOX6). The high number of discoveries made with modest genotyping effort can be attributed to using a large-scale yet targeted genotyping array and to the development of a weighting scheme that maximized power when meta-analyzing results from samples ascertained with extreme phenotypes, in combination with results from nonascertained or population samples. Chromatin immunoprecipitation and transcript expression data highlight potential gene regulatory mechanisms at the MTHFR and NOS3 loci. These results provide candidates for further study to help dissect mechanisms affecting BP and highlight the utility of studying SNPs and samples that are independent of those studied previously even when the sample size is smaller than that in previous studies.

Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables

Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.

RF tumour ablation: Computer simulation and mathematical modelling of the effects of electrical and thermal conductivity

This study determined the effects of thermal conductivity on RF ablation tissue heating using mathematical modelling and computer simulations of RF heating coupled to thermal transport. Computer simulation of the Bio-Heat equation coupled with temperature-dependent solutions for RF electric fields (ETherm) was used to generate temperature profiles 2 cm away from a 3 cm internally-cooled electrode. Multiple conditions of clinically relevant electrical conductivities (0.07-12 S m-1) and 'tumour' radius (5-30 mm) at a given background electrical conductivity (0.12 S m-1) were studied. Temperature response surfaces were plotted for six thermal conductivities, ranging from 0.3-2 W m-1 degrees C (the range of anticipated clinical and experimental systems). A temperature response surface was obtained for each thermal conductivity at 25 electrical conductivities and 17 radii (n=425 temperature data points). The simulated temperature response was fit to a mathematical model derived from prior phantom data. This mathematical model is of the form (T=a+bRc exp(dR) s(f) exp(g)(s)) for RF generator-energy dependent situations and (T=h+k exp(mR)+n?exp(p)(s)) for RF generator-current limited situations, where T is the temperature (degrees C) 2 cm from the electrode and a, b, c, d, f, g, h, k, m, n and p are fitting parameters. For each of the thermal conductivity temperature profiles generated, the mathematical model fit the response surface to an r2 of 0.97-0.99. Parameters a, b, c, d, f, k and m were highly correlated to thermal conductivity (r2=0.96-0.99). The monotonic progression of fitting parameters permitted their mathematical expression using simple functions. Additionally, the effect of thermal conductivity simplified the above equation to the extent that g, h, n and p were found to be invariant. Thus, representation of the temperature response surface could be accurately expressed as a function of electrical conductivity, radius and thermal conductivity. As a result, the non-linear temperature response of RF induced heating can be adequately expressed mathematically as a function of electrical conductivity, radius and thermal conductivity. Hence, thermal conductivity accounts for some of the previously unexplained variance. Furthermore, the addition of this variable into the mathematical model substantially simplifies the equations and, as such, it is expected that this will permit improved prediction of RF ablation induced temperatures in clinical practice.

Computer modeling of the effect of perfusion on heating patterns in radiofrequency tumor ablation

PURPOSE

To use an established computer simulation model of radiofrequency (RF) ablation to further characterize the effect of varied perfusion on RF heating for commonly used RF durations and electrode types, and different tumor sizes.

METHODS

Computer simulation of RF heating using 2-D and 3-D finite element analysis (Etherm) was performed. Simulated RF application was systematically modeled on clinically relevant application parameters for a range of inner tumor perfusion (0-5 kg/m3-s) and outer normal surrounding tissue perfusion (0-5 kg/m3-s) for internally cooled 3-cm single and 2.5-cm cluster electrodes over a range of tumor diameters (2-5 cm), and RF application times (5-60 min; n = 4618 simulations). Tissue heating patterns and the time required to heat the entire tumor +/- a 5-mm margin to > 50 degrees C were assessed. Three-dimensional surface response contours were generated, and linear and higher order curve-fitting was performed.

RESULTS

For both electrodes, increasing overall tissue perfusion exponentially decreased the overall distance of the 50 degrees C isotherm (R2 = 0.94). Simultaneously, increasing overall perfusion exponentially decreased the time required to achieve thermal equilibrium (R2 = 0.94). Furthermore, the relative effect of inner and outer perfusion varied with increasing tumor size. For smaller tumors (2 cm diameter, 3-cm single; 2-3 cm diameter, cluster), the ability and time to achieve tumor ablation was largely determined by the outer tissue perfusion value. However, for larger tumors (4-5 cm diameter single; 5 cm diameter cluster), inner tumor perfusion had the predominant effect.

CONCLUSION

Computer modeling demonstrates that perfusion reduces both RF coagulation and the time to achieve thermal equilibrium. These results further show the importance of considering not only tumor perfusion, but also size (in addition to background tissue perfusion) when attempting to predict the effect of perfusion on RF heating and ablation times.

Dietary intake and gene variation influence the response of plasma lipids to dietary intervention

We have examined whether variation at the apolipoprotein (apo) B, apo E, apo AII, and apo AI-CIII-AIV genes affected the relationship between dietary intake and serum lipid traits in individuals who had participated in dietary intervention from a basal high fat diet to a low fat diet followed by a return to their natural diet, the switchback. On both the basal and switchback diets where the variance of dietary intake was great, there was a significant correlation between P/S ratio and serum total, low-density lipoprotein (LDL) cholesterol, and apo AI levels. In addition dietary cholesterol (dchol) levels correlated significantly with serum apo AI levels on the basal diet. Comparing the difference between basal and intervention (delta 1) and between switchback and intervention diets (delta 2), changes in dchol and P/S ratio correlated significantly with changes in serum total, high-density lipoprotein (HDL) and LDL cholesterol, and apo B levels. There was a significant correlation between monounsaturated fatty acid (MUFA) and apo AI levels during both changes. Furthermore we have examined whether the relationship between variables was homogeneous among genotypes of candidate gene polymorphisms. A heterogeneous effect (P less than 0.01) was seen among genotypes of the PvuII-AIV restriction fragment length polymorphism (RFLP) on the correlation of serum LDL cholesterol levels and dietary MUFA during both dietary changes (delta 1 and delta 2). A heterogeneous effect among genotypes of the apo B XbaI RFLP on the correlation between dchol versus total and LDL cholesterol during the change delta 1, but not delta 2, was observed. Thus our results show that both dietary components and genetic variation affect the response of serum lipid, lipoprotein, and apolipoprotein levels to dietary change.