Time Below Range and Its Influence on Hypoglycemia Awareness and Severe Hypoglycemia: Insights From the Association of British Clinical Diabetologists Study

OBJECTIVE This study aimed to explore the relationship between time below range (TBR), impaired awareness of hypoglycemia (IAH), and severe hypoglycemia (SH). RESEARCH DESIGN AND METHODS This cross-sectional study analyzed data from individuals with diabetes using continuous glucose monitors (CGMs) in the Association of British Clinical Diabetologists audit. Hypoglycemia awareness was assessed via the Gold score (≥4 denoting IAH), and SH was defined as hypoglycemia requiring third-party assistance. Logistic regression was used to determine the association between TBR percentage (<70 mg/dL; 3.9 mmol/L) at first follow-up and follow-up Gold score and SH incidence. The Youden J index identified optimal TBR percentage cutoffs for detecting IAH and SH. RESULTS The study included 15,777 participants, with follow-up TBR and SH data available for 5,029. The median TBR percentage was 4% (interquartile range 2–6.6%), with 42% meeting the recommended TBR of ≤4%. Adjusted for age, sex, and BMI, TBR was significantly associated with SH (P < 0.001) and IAH (P = 0.005). Optimal TBR cutoffs for identifying IAH and SH were 3.35% and 3.95%, yielding negative predictive value (NPV) values of 85% and 97%, respectively. CONCLUSIONS Our findings support the international consensus recommending a TBR of </p

Summarised results of GLMM explaining variance in annual counts of hedgehogs on amenity grassland in relation to experimental badger culling.

<p>Summarised results of GLMM explaining variance in annual counts of hedgehogs on amenity grassland in relation to experimental badger culling.</p

Manhattan plot for the FBAT-CNV P-values.

<p>The y-axis shows the distribution of –log₁₀(p) where p is the FBAT-CNV test association test P-value for all CNV loci passing quality control filters (Methods). The x-axis shows chromosomes numbered from 1 (left) to X (right).</p

Mean hedgehog density on amenity grassland fields during the Randomised Badger Culling Trial.

<p>Shaded columns show badger culling areas and white columns show experimental control areas.</p

The number of years of hedgehog surveys that were carried out in each triplet.

<p>Estimates of the reduction in badger density are after Smith & Cheeseman (2007).</p

Top ten T1D associated CNVs after removing known loci and technical artifacts.

<p>The last column lists the genes for which at least one exon overlaps the defined CNV region. P-value refers to the FBAT association test for autosomal CNVs, and to the FBAT-X association test otherwise.</p

Decomposition of multi-probe CNV data at the <i>INS</i> VNTR locus into first two principal components PC1 and PC2.

<p>Principal components PC1 and PC2 summarize the multi-probe CNV data at the <i>INS</i> VNTR locus. Colors (green/red/black) were chosen based on the genotypes of the SNP rs689 (AA/AT/TT), which captures the class I-class III separation.</p

Differences between case-control and FBAT-CNV association tests.

<p>A- In a case-control analysis, technical variability may affect the CNV intensity data between cases and controls. Therefore, it is necessary to call the discrete genotypes, potentially allowing for genotype uncertainty in the association tests. Mixture models are typically used for calling, as illustrated by the colored lines on top of the histograms. Intensity data must therefore be sufficiently separated to make these discrete calls (CNV data in this example obtained from both control groups in the WTCCC study <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004367#pgen.1004367-WTCCC1" target="_blank">[28]</a>). B- With the FBAT-CNV framework, one compares the average parental CNV signal with the signal for affected offspring. Consistent deviation of affected offspring intensity data compared to parental average indicates biased transmission of CNV alleles. As the test is solely based on the intensity data, and no systematic bias is expected between parents and offspring, it is not necessary to make discrete calls (CNV data obtained from INS VNTR first principal component).</p

Quantile-quantile plot comparing the expected versus the observed distribution of the FBAT-CNV P-values.

<p>These plots show the distribution of -2log₁₀(p), which is, under the null, distributed as chi-square with 2 degrees-of-freedom. IgG/TCR loci are discussed elsewhere and not included in these plots. A – N = 3,286 CNVs that passed quality controls and were tested for association. Loci overlapping the MHC region are marked in blue. Loci mapping to, or in strong LD with, the <i>INS</i> VNTR region are marked in red. B – N = 3,214 CNVs passed quality controls and did not overlap or tagged the <i>INS</i> VNTR and the MHC region. C – N = 448 VNTRs targeted by the CGH array that passed quality controls. <i>INS</i> VNTR CNV regions are marked in red as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004367#pgen-1004367-g003" target="_blank">Figure 3A</a>.</p

Summary of the CNVs included in the array design and tested for T1D association using FBAT-CNV.

<p>CNVs originate from two main sources: the GSV map of common CNVs <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004367#pgen.1004367-Conrad2" target="_blank">[27]</a> and the 1,000 Genomes sequence data. Tested CNVs also include 365 novel insertion CNVs obtained from the Venter genome. Detailed description of the array design is provided in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004367#pgen.1004367.s016" target="_blank">Text S1</a>.</p