Scant attention has been paid to evaluating differences in the prevalence of comorbidities and diabetes-related complications in familial versus sporadic type 1 diabetes (1). Knowledge gains in this area could advance the development of risk prediction tools and tailored interventions for preventing or delaying onset of comorbidities or diabetes-related complications in high-risk patient subgroups.

To address this gap, we applied a computationally optimized, exploratory data mining algorithm to the T1D Exchange Clinic Registry (2). For the first time in a large U.S.-based cohort, we assessed demographic and phenotypic factors and comorbid conditions for associations with familial (i.e., having an affected first-degree relative) or sporadic (i.e., having no family history of type 1 diabetes) disease.

The T1D Exchange Clinic Registry is a deidentified, publicly available data set comprising 34,013 adult and pediatric participants who received routine clinical care at 83 U.S.-based endocrinology practices between July 2007 and April 2018 (3). We analyzed participants with a family history of type 1 diabetes involving a first-degree relative, i.e., father (n = 1,464), mother (n = 818), sibling/twin (n = 1,882), and/or child (n = 228) (total n = 3,941) or no family history of type 1 diabetes (n = 12,291). Excluding participants >50 years old resulted in a relatively balanced distribution of age and diabetes duration across both subgroups.

A contrast pattern mining algorithm detects significant differences in the frequencies of attributes across two patient subgroups. We used our validated algorithm to discover individual and co-occurring characteristics that were documented significantly more frequently in familial versus sporadic type 1 diabetes. Here, we refer to these characteristics as “patterns” or “feature patterns.” Our algorithm returns feature patterns consisting of one, two, or three elements. Individual elements are synonymous with individual characteristics.

Metrics used in feature pattern analysis include support, growth, and confidence (4,5). Support is the proportion of individuals in a subgroup who are associated with a given feature pattern. Growth is a support ratio between subgroups. Confidence corresponds to the statistical concept of positive predictive value. We used Fisher exact tests to calculate the statistical significance of each pattern (P < 0.05) and the Benjamini-Hochberg (BH) procedure to control for false discovery (false discovery rate of 0.1).

Of 16,232 individuals who met inclusion criteria, 24.3% (n = 3,941) had an affected first-degree relative. Median age of familial cases was 18 (interquartile range [IQR] 15, 27) years; for sporadic cases, median age was 18 (IQR 15, 23) years (P = 0.05). Median diabetes duration in familial cases was 10 (IQR 6, 16) years; in sporadic cases, median diabetes duration was 9 (IQR, 6, 14) years (P < 0.001). Median age at diagnosis was 8 (IQR 4, 12) years in both subgroups (P = 0.002). Mean (± SD) hemoglobin A1c (HbA1c) for familial cases was 8.4 ± 1.3% (68.7 ± 14.7 mmol/mol); for sporadic cases, mean HbA1c was 8.3 ± 1.2% (66.72 ± 13.2 mmol/mol) (P < 0.001).

We discovered 590 feature patterns that met a minimum prevalence threshold of 1% in at least one subgroup. After controlling for false discovery, 265 patterns retained statistical significance. These included 29 single-element patterns, 103 two-element patterns, and 133 three-element patterns (Table 1).

Table 1

Enrichment of phenotypic characteristics and comorbid conditions in familial and sporadic type 1 diabetes

Feature patternEnriched subgroup*Support: enriched subgroup (%)Growth: enriched subgroupConfidence: enriched subgroupNonenriched subgroupSupport: nonenriched subgroup (%)Growth: nonenriched subgroupConfidence: nonenriched subgroupP value
One-element feature patterns          
 No documented comorbidities Sporadic 27.28 1.35 0.81 Familial 20.15 0.19 0.19 1.08E-19 
 Hypertension Familial 12.00 1.46 0.32 Sporadic 8.24 0.68 0.68 4.00E-12 
 Asian Sporadic 1.64 3.08 0.91 Familial 0.53 0.32 0.09 1.52E-08 
 Non-Hispanic Black Familial 6.29 1.55 0.33 Sporadic 4.07 0.67 0.67 2.03E-08 
 Hyperlipidemia/ dyslipidemia Familial 21.52 1.22 0.28 Sporadic 17.57 0.72 0.72 4.49E-08 
 Atherosclerosis Familial 1.14 3.26 0.51 Sporadic 0.35 0.31 0.49 5.53E-08 
 RMV disorder Familial 9.06 1.36 0.30 Sporadic 6.68 0.70 0.70 1.07E-06 
 Diagnosis age 0–4 years Familial 29.21 1.15 0.27 Sporadic 25.30 0.73 0.73 1.53E-06 
 Erectile/sexual dysfunction Familial 1.50 2.12 0.40 Sporadic 0.71 0.47 0.60 1.57E-05 
 Gastroesophageal reflux disease Familial 3.15 1.60 0.34 Sporadic 1.97 0.66 0.66 3.31E-05 
 Substance abuse disorder Familial 1.22 2.11 0.40 Sporadic 0.58 0.47 0.60 9.69E-05 
 Neuropathy Familial 4.16 1.45 0.32 Sporadic 2.87 0.68 0.68 1.09E-04 
 Diagnosis age ≥26 years Familial 4.47 1.42 0.31 Sporadic 3.15 0.69 0.69 1.38E-04 
 Nephropathy Familial 4.52 1.36 0.30 Sporadic 3.31 0.70 0.70 5.74E-04 
 Insomnia Familial 1.02 2.05 0.40 Sporadic 0.50 0.49 0.60 6.46E-04 
 Depression Familial 11.70 1.18 0.27 Sporadic 9.93 0.73 0.73 1.78E-03 
 Anemia Familial 1.62 1.62 0.34 Sporadic 1.00 0.66 0.66 1.97E-03 
 Diagnosis age 13–18 years Sporadic 14.41 1.15 0.78 Familial 12.51 0.22 0.22 2.59E-03 
 ADHD Familial 7.71 1.20 0.28 Sporadic 6.44 0.72 0.72 6.21E-03 
 Diagnosis age 5–9 years Sporadic 34.27 1.07 0.77 Familial 32.00 0.23 0.23 8.95E-03 
 Thyroid disorder Familial 21.31 1.10 0.26 Sporadic 19.40 0.74 0.74 9.53E-03 
 Diagnosis age 10–12 years Sporadic 18.84 1.11 0.78 Familial 17.03 0.22 0.22 1.07E-02 
 Allergy Familial 5.33 1.23 0.28 Sporadic 4.34 0.72 0.72 1.11E-02 
 Sleep apnea syndrome Familial 1.22 1.54 0.33 Sporadic 0.79 0.65 0.67 1.50E-02 
 Constipation Familial 1.73 1.43 0.31 Sporadic 1.20 0.69 0.69 1.63E-02 
 Hispanic or Latino Sporadic 8.92 1.16 0.78 Familial 7.71 0.22 0.22 1.89E-02 
 Overweight/obesity Familial 4.95 1.20 0.28 Sporadic 4.13 0.72 0.72 3.09E-02 
 Asthma Familial 6.06 1.17 0.27 Sporadic 5.17 0.73 0.73 3.50E-02 
 Diagnosis age 19–25 years Familial 4.77 1.18 0.28 Sporadic 4.03 0.72 0.72 4.49E-02 
 Selected two- and three-element feature patterns          
 Hyperlipidemia/ dyslipidemia and hypertension Familial 6.95 1.59 0.34 Sporadic 4.36 0.66 0.66 4.07E-10 
 No documented comorbidities and diagnosis age 5–9 years Sporadic 9.15 1.46 0.82 Familial 6.27 0.18 0.18 6.46E-09 
 RMV disorder and hyperlipidemia/dyslipidemia Familial 4.95 1.58 0.34 Sporadic 3.14 0.66 0.66 2.71E-07 
 RMV disorder and hypertension Familial 3.88 1.68 0.35 Sporadic 2.31 0.65 0.65 3.17E-07 
 Hyperlipidemia/ dyslipidemia and hypertension and RMV disorder Familial 2.84 1.81 0.37 Sporadic 1.57 0.63 0.63 1.01E-06 
 No documented comorbidities and diagnosis age 13–18 years Sporadic 4.38 1.55 0.83 Familial 2.82 0.17 0.17 8.52E-06 
 No documented comorbidities and diagnosis age 10–12 Sporadic 5.16 1.46 0.82 Familial 3.53 0.18 0.18 2.00E-05 
 Nephropathy and hypertension Familial 2.64 1.64 0.34 Sporadic 1.61 0.66 0.66 6.01E-05 
 Nephropathy and hyperlipidemia/dyslipidemia Familial 2.54 1.65 0.35 Sporadic 1.54 0.65 0.65 7.31E-05 
 Diagnosis age 5–9 years and RMV disorder Familial 3.17 1.53 0.33 Sporadic 2.07 0.67 0.67 1.26E-04 
 Neuropathy and hyperlipidemia/dyslipidemia Familial 2.51 1.62 0.34 Sporadic 1.55 0.66 0.66 1.36E-04 
 Depression and hypertension Familial 2.51 1.55 0.33 Sporadic 1.62 0.67 0.67 4.80E-04 
 No documented comorbidities and Hispanic or Latino Sporadic 2.87 1.49 0.82 Familial 1.93 0.18 0.18 1.11E-03 
 RMV disorder and thyroid disorder Familial 2.56 1.41 0.31 Sporadic 1.81 0.69 0.69 4.78E-03 
Feature patternEnriched subgroup*Support: enriched subgroup (%)Growth: enriched subgroupConfidence: enriched subgroupNonenriched subgroupSupport: nonenriched subgroup (%)Growth: nonenriched subgroupConfidence: nonenriched subgroupP value
One-element feature patterns          
 No documented comorbidities Sporadic 27.28 1.35 0.81 Familial 20.15 0.19 0.19 1.08E-19 
 Hypertension Familial 12.00 1.46 0.32 Sporadic 8.24 0.68 0.68 4.00E-12 
 Asian Sporadic 1.64 3.08 0.91 Familial 0.53 0.32 0.09 1.52E-08 
 Non-Hispanic Black Familial 6.29 1.55 0.33 Sporadic 4.07 0.67 0.67 2.03E-08 
 Hyperlipidemia/ dyslipidemia Familial 21.52 1.22 0.28 Sporadic 17.57 0.72 0.72 4.49E-08 
 Atherosclerosis Familial 1.14 3.26 0.51 Sporadic 0.35 0.31 0.49 5.53E-08 
 RMV disorder Familial 9.06 1.36 0.30 Sporadic 6.68 0.70 0.70 1.07E-06 
 Diagnosis age 0–4 years Familial 29.21 1.15 0.27 Sporadic 25.30 0.73 0.73 1.53E-06 
 Erectile/sexual dysfunction Familial 1.50 2.12 0.40 Sporadic 0.71 0.47 0.60 1.57E-05 
 Gastroesophageal reflux disease Familial 3.15 1.60 0.34 Sporadic 1.97 0.66 0.66 3.31E-05 
 Substance abuse disorder Familial 1.22 2.11 0.40 Sporadic 0.58 0.47 0.60 9.69E-05 
 Neuropathy Familial 4.16 1.45 0.32 Sporadic 2.87 0.68 0.68 1.09E-04 
 Diagnosis age ≥26 years Familial 4.47 1.42 0.31 Sporadic 3.15 0.69 0.69 1.38E-04 
 Nephropathy Familial 4.52 1.36 0.30 Sporadic 3.31 0.70 0.70 5.74E-04 
 Insomnia Familial 1.02 2.05 0.40 Sporadic 0.50 0.49 0.60 6.46E-04 
 Depression Familial 11.70 1.18 0.27 Sporadic 9.93 0.73 0.73 1.78E-03 
 Anemia Familial 1.62 1.62 0.34 Sporadic 1.00 0.66 0.66 1.97E-03 
 Diagnosis age 13–18 years Sporadic 14.41 1.15 0.78 Familial 12.51 0.22 0.22 2.59E-03 
 ADHD Familial 7.71 1.20 0.28 Sporadic 6.44 0.72 0.72 6.21E-03 
 Diagnosis age 5–9 years Sporadic 34.27 1.07 0.77 Familial 32.00 0.23 0.23 8.95E-03 
 Thyroid disorder Familial 21.31 1.10 0.26 Sporadic 19.40 0.74 0.74 9.53E-03 
 Diagnosis age 10–12 years Sporadic 18.84 1.11 0.78 Familial 17.03 0.22 0.22 1.07E-02 
 Allergy Familial 5.33 1.23 0.28 Sporadic 4.34 0.72 0.72 1.11E-02 
 Sleep apnea syndrome Familial 1.22 1.54 0.33 Sporadic 0.79 0.65 0.67 1.50E-02 
 Constipation Familial 1.73 1.43 0.31 Sporadic 1.20 0.69 0.69 1.63E-02 
 Hispanic or Latino Sporadic 8.92 1.16 0.78 Familial 7.71 0.22 0.22 1.89E-02 
 Overweight/obesity Familial 4.95 1.20 0.28 Sporadic 4.13 0.72 0.72 3.09E-02 
 Asthma Familial 6.06 1.17 0.27 Sporadic 5.17 0.73 0.73 3.50E-02 
 Diagnosis age 19–25 years Familial 4.77 1.18 0.28 Sporadic 4.03 0.72 0.72 4.49E-02 
 Selected two- and three-element feature patterns          
 Hyperlipidemia/ dyslipidemia and hypertension Familial 6.95 1.59 0.34 Sporadic 4.36 0.66 0.66 4.07E-10 
 No documented comorbidities and diagnosis age 5–9 years Sporadic 9.15 1.46 0.82 Familial 6.27 0.18 0.18 6.46E-09 
 RMV disorder and hyperlipidemia/dyslipidemia Familial 4.95 1.58 0.34 Sporadic 3.14 0.66 0.66 2.71E-07 
 RMV disorder and hypertension Familial 3.88 1.68 0.35 Sporadic 2.31 0.65 0.65 3.17E-07 
 Hyperlipidemia/ dyslipidemia and hypertension and RMV disorder Familial 2.84 1.81 0.37 Sporadic 1.57 0.63 0.63 1.01E-06 
 No documented comorbidities and diagnosis age 13–18 years Sporadic 4.38 1.55 0.83 Familial 2.82 0.17 0.17 8.52E-06 
 No documented comorbidities and diagnosis age 10–12 Sporadic 5.16 1.46 0.82 Familial 3.53 0.18 0.18 2.00E-05 
 Nephropathy and hypertension Familial 2.64 1.64 0.34 Sporadic 1.61 0.66 0.66 6.01E-05 
 Nephropathy and hyperlipidemia/dyslipidemia Familial 2.54 1.65 0.35 Sporadic 1.54 0.65 0.65 7.31E-05 
 Diagnosis age 5–9 years and RMV disorder Familial 3.17 1.53 0.33 Sporadic 2.07 0.67 0.67 1.26E-04 
 Neuropathy and hyperlipidemia/dyslipidemia Familial 2.51 1.62 0.34 Sporadic 1.55 0.66 0.66 1.36E-04 
 Depression and hypertension Familial 2.51 1.55 0.33 Sporadic 1.62 0.67 0.67 4.80E-04 
 No documented comorbidities and Hispanic or Latino Sporadic 2.87 1.49 0.82 Familial 1.93 0.18 0.18 1.11E-03 
 RMV disorder and thyroid disorder Familial 2.56 1.41 0.31 Sporadic 1.81 0.69 0.69 4.78E-03 

Two categories of results were used: 1) one-element feature patterns and 2) two- and three-element feature patterns. P values were obtained using Fisher exact tests. False discovery resulting from multiple-hypothesis testing was controlled using the BH procedure (false discovery rate, 0.1). Results in both categories (i.e., one-element feature patterns and two- and three-element feature patterns) are sorted by P value. Two- and three-element patterns selected for inclusion in this table met the following criteria: 1) confidence was increased relative to related one-element patterns, 2) pattern growth in the enriched subgroup was ≥1.4, 3) pattern support in the enriched subgroup was ≥2.5, and 4) individual pattern elements previously retained significance (as one-element patterns) following use of the BH procedure. ADHD, attention deficit/hyperactivity disorder.

*

Enriched subgroup is the subgroup in which the feature pattern was documented more frequently.

View Large

Conditions that were significantly enriched in familial type 1 diabetes included hypertension, hyperlipidemia/dyslipidemia, atherosclerosis, retinopathy/maculopathy/vitreopathy (RMV), erectile and sexual dysfunction, gastroesophageal reflux disease, neuropathy, and nephropathy. A higher proportion of individuals with familial disease (vs. sporadic disease) were non-Hispanic Black (6.3% vs. 4.1%). Sporadic type 1 diabetes was more frequently associated with the absence of other medical conditions, Asian race, Hispanic ethnicity, and diagnosis at ages 5–9, 10–12, and 13–18 years.

Hyperlipidemia/dyslipidemia and hypertension, combined, were present for 7.0% of familial cases but for only 4.4% of sporadic cases. Co-occurring RMV and hyperlipidemia/dyslipidemia were documented for 5.0% of familial cases and for 3.1% of sporadic cases.

In contrast to most earlier studies, this study did not exclude patients diagnosed with type 1 diabetes as adults. Across the two subgroups, the difference in median diabetes duration was small (∼1 year) and mean HbA1c was similar, suggesting that the observed associations cannot be completely explained by the small difference in diabetes duration and HbA1c. An important limitation is that the Registry does not identify whether more than one participant originated from the same family unit; therefore, individual family units may be represented in this analysis more than once.

This study of more than 16,200 individuals in the T1D Exchange Clinic Registry is the largest study to date to evaluate longitudinal health outcomes in individuals with familial versus sporadic type 1 diabetes. Further research is needed to validate the present results in a large population-based cohort.

Acknowledgments. The authors thank all the participants and clinicians of the T1D Exchange Clinic Registry who, through their participation, continue to advance understanding of type 1 diabetes. We also thank Dr. Noah Greifer, Johns Hopkins Bloomberg School of Public Health, for his assistance and valuable comments regarding patient matching methods.

The source of the data used in this analysis is the T1D Exchange, but the analyses, content, and conclusions presented here are solely the responsibility of the authors and have not been reviewed or approved by the T1D Exchange.

Funding. E.M.T. is supported by a grant from the U.S. National Library of Medicine of the National Institutes of Health (5T32LM012410). The T1D Exchange Clinic Registry was originally created through support from the Leona M. and Harry B. Helmsley Charitable Trust. M.A.C. is supported by an independent grant from the Leona M. and Harry B. Helmsley Charitable Trust (G-2008-04043). The computation for this work was performed on the high-performance computing infrastructure provided by Research Computing Support Services and in part by the National Science Foundation under grant number CNS-1429294 at the University of Missouri, Columbia, MO (https://doi.org/10.32469/10355/69802).

The contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.

Duality of Interest. M.A.C. is the chief medical officer at Glooko. He receives research support from Dexcom and Abbott Diabetes Care. C.-R.S. is a consultant for Curant Health. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. E.M.T. designed the study, performed data cleaning and analysis, and wrote the manuscript. M.J.R. contributed to the discussion pertaining to methodology and reviewed and edited the manuscript. C.-R.S. contributed to the discussion and design of data analytics, reviewed and edited the manuscript, and provided funding for E.M.T., D.L., and K.B. D.L. developed the algorithm used to conduct the analysis, contributed to the discussion, and reviewed and edited the manuscript. K.B. contributed to the discussion, performed data cleaning and analysis, developed data visualizations, and reviewed and edited the manuscript. M.A.C. contributed to the discussion, assisted with data mapping and results interpretation, and reviewed and edited the manuscript. E.M.T. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented at the National Library of Medicine Annual Informatics Training Meeting, 22–24 June 2020.

1.
Lebenthal
Y
,
Shalitin
S
,
Yackobovitch-Gavan
M
,
Phillip
M
,
Lazar
L
.
Retrospective comparative analysis of metabolic control and early complications in familial and sporadic type 1 diabetes patients
.
J Diabetes Complications
2012
;
26
:
219
–
224
Google Scholar
Crossref
Search ADS
PubMed
 
2.
Liu
D
,
Baskett
W
,
Beversdorf
D
,
Shyu
C-R
.
Exploratory data mining for subgroup cohort discoveries and prioritization
.
IEEE J Biomed Health Inform
2020
;
24
:
1456
–
1468
Google Scholar
Crossref
Search ADS
PubMed
 
3.
Beck
RW
,
Tamborlane
WV
,
Bergenstal
RM
,
Miller
KM
,
DuBose
SN
;
T1D Exchange Clinic Network
.
The T1D Exchange clinic registry
.
J Clin Endocrinol Metab
2012
;
97
:
4383
–
4389
Google Scholar
Crossref
Search ADS
PubMed
 
4.
Larose
DT
,
Larose
CD
.
Discovering Knowledge in Data: An Introduction to Data Mining
.
2nd edition, John Wiley & Sons, Inc., 2014. Accessed 20 April 2021. Available from https://www.onlinelibrary.wiley.com/doi/book/10.1002/9781118874059
5.
Dong
G
,
Li
J
.
Efficient mining of emerging patterns: discovering trends and differences
. In
Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD '99, 1999
.
New York
,
NY, Association for Computing Machinery
, p.
43
–
52
Crossref
Search ADS
 
© 2022 by the American Diabetes Association
2022
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.