Differences in lipid and blood pressure measurements between individuals with type 1 diabetes and the general population

doi:10.21203/rs.3.rs-2442331/v1

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Differences in lipid and blood pressure measurements between individuals with type 1 diabetes and the general population

https://doi.org/10.21203/rs.3.rs-2442331/v1

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Background:

Cardiovascular disease (CVD) is a precarious complication of type 1 diabetes (T1D). Alongside glycaemic control, lipid and blood pressure (BP) management are essential for the prevention of CVD. However, age-specific differences in lipid and BP between individuals with T1D and the general population are relatively unknown. We therefore compared total-, LDL-cholesterol, systolic BP (SBP) and diastolic BP (DBP) in individuals with T1D and the general population.

Methods:

Cross-sectional data from individuals with T1D visiting six outpatient clinics in the Netherlands were compared to a background population without diabetes from the Lifelines cohort, a multigenerational cohort from the Northern Netherlands. Descriptive statistics were calculated stratified by age group, sex and medication use.

Results:

In total 2,178 individuals with T1D and 146,822 without diabetes were included in this study. Total- and LDL-cholesterol were lower and SBP and DBP were higher in individuals with T1D in comparison to the background population. When stratified by age and medication use, total- and LDL-cholesterol were lower and SBP and DBP higher in the T1D population. Males with T1D achieved lower LDL-cholesterol levels both with and without medication in older age groups in comparison to females. Females with T1D had up to 8mmHg higher SBP compared to the background population, this difference was not present in males.

Conclusion:

Lipid and BP measurements are not comparable between individuals with T1D and the general population and are particularly unfavourable for BP in the T1D group. There are potential sex differences in the management of LDL-cholesterol and BP.

Diabetes Mellitus

Type 1

Cholesterol

LDL

Blood Pressure

Cardiovascular Diseases

Antihypertensive Agents

Anticholesteraemic Agents

Cardiometabolic risk

Individuals with type 1 diabetes (T1D) have an increased risk of all-cause mortality, particularly cardiovascular mortality (1–6). Cardiovascular disease (CVD) prevention is therefore an essential part of T1D management. Although the exact pathophysiology remains unclear, glycaemic control plays an important role in the development of CVD (7, 8). Management of other classical risk factors such as elevated lipid and blood pressure levels further contribute to preventing CVD, both in the general population and in individuals with type 2 diabetes (9). Intervention studies with statins and blood pressure lowering agents in T1D have demonstrated only moderate effects on CVD risk (10). Nevertheless, current cardiovascular risk management in individuals with T1D consists of glycaemic, lipid and blood pressure management (9, 11).

Although a decrease in CVD-related hospitalisations and mortality in individuals with T1D has been described over time, there remains a significant excess incidence of CVD in comparison to controls (12). Yet, despite these differences, research on non-glycaemic strategies in CVD risk reduction in individuals with T1D is limited. Management strategies have largely been extrapolated from prevention trials conducted in individuals with type 2 diabetes and these inferences rely on the assumption that the effects of CVD risk factors in these two populations are comparable (12, 13). However, differences in lipid profiles, dyslipidaemia, and hypertension have been reported between individuals with T1D, type 2 diabetes (T2D), and the background population (14).

T1D primarily develops in younger individuals, resulting in a longer lifetime exposure to potential CVD risk factors such as hyper- and hypoglycaemia, dyslipidaemia, and hypertension (15, 16). Little is known about lipid profiles and blood pressure across age groups in T1D. Comparing the lipid and blood pressures of individuals with T1D to a background population can help to benchmark any differences existing between those with and without T1D. Furthermore, it may provide insights to an ideal starting point for pharmacological intervention. In this study, we report the lipid profiles and blood pressure over different age decades in adults with T1D and compare these to individuals without diabetes from the general population.

Population

Type 1 Diabetes population: Diabeter/UMCG study

Data from individuals with T1D visiting Diabeter, a specialised T1D outpatient clinic with five separate locations in The Netherlands, and the University Medical Center Groningen were used in this study. Cross-sectional data were collected in 2018 from electronic medical files, as described earlier (17). Individuals were included if they were diagnosed with T1D, were ≥ 18 years of age, and had been using insulin for ≥ 1 year.

The Medical Ethical Review Board of the UMCG, Groningen, the Netherlands, declared that the study was not subject to the Dutch ‘Medical Research Involving Human Subjects Act’ (WMO) and a waiver was granted. The institutional review board approved the study protocol (202000883).

Control population: Lifelines Cohort study

Data obtained in individuals without diabetes mellitus from the general population, who participated in the Lifelines Cohort study were used as an age-matched comparison. Lifelines is a multi-disciplinary prospective population-based cohort study examining in a unique three-generation design the health and health-related behaviours of 167,729 persons living in the North of the Netherlands. It employs a broad range of investigative procedures in assessing the biomedical, socio-demographic, behavioural, physical and psychological factors which contribute to the health and disease of the general population, with a special focus on multi-morbidity and complex genetics. Individuals who were measured at baseline between 2007 and 2014, and were ≥ 18 years of age without a diagnosis of Type 1 or Type 2 diabetes were included for analysis (18, 19).

The study protocol was approved by the medical ethical review committee of the University Medical Center Groningen, Groningen, the Netherlands.

Variables and definition

Data extracted included age, sex, ethnicity, body mass index (BMI), smoking behaviour, blood pressure, total and LDL-cholesterol, lipid lowering medication (LLM) use, antihypertensive medication (AHM) use, and the presence of cardiovascular disease. The latter was defined as the presence of coronary artery disease, or an earlier cerebrovascular accident or transient ischaemic attack. Presence of peripheral arterial disease was excluded, as this information was not available in the Lifelines Cohort Study.

Total and LDL-cholesterol were measured in a routine outpatient clinical setting for the T1D population and were generally non-fasting measurements. In the Lifelines cohort study, total and LDL-cholesterol were collected the morning after an overnight fast. For both populations serum total cholesterol was measured using an enzymatic colorimetric method and the LDL-cholesterol using an enzymatic method on the Roche Modular P chemistry analyser (Roche, Basel, Switzerland).

In the Lifelines cohort, the blood pressure measurements were conducted during the first study visit. Individuals were measured every minute for 10 minutes, and the mean of the last three measurements is reported. The blood pressure measurements from the T1D cohort were performed during an outpatient clinic visit and extracted from the electronic medical files.

Medication use was derived from electronic medical records in the T1D population. In the Lifelines cohort, medication was self-reported.

Statistical Analysis

All statistical analyses were conducted using R Statistical Software, R Studio Software and R packages (20–24). Descriptive data of the populations are presented where appropriate as means with standard deviations, medians with interquartile range, or counts with frequencies.

For each population, the mean and 95% confidence intervals for total and LDL-cholesterol, and systolic (SBP) and diastolic blood pressure (DBP) were calculated stratified by medication use and for the following age categories: “18–30”, “30–40”, “40–50”, “50–60”, “60–70” and “70+”. Additionally, LDL-cholesterol and SBP means, and 95% confidence intervals were calculated stratified by sex. Finally, CVD prevalence, LLM and AHM use were calculated for the age categories specified above.

From a total of 2,287 individuals with T1D from the Diabeter/UMCG study, 2,178 individuals had complete data on total cholesterol, LDL-cholesterol, SBP and DBP. From the Lifelines Cohort, 146,822 individuals without diabetes and complete data were included in the analysis. Table 1 presents the characteristics of these two study populations.

Table 1

*Characteristics of participants with type 1 diabetes and individuals without diabetes from the Lifelines Cohort Study.*
	Type 1 Diabetes Diabeter/UMCG Study N = 2,178	Background population Lifelines Cohort Study N = 146,822
Age, years	27 [22–43]	44 [35–51]
Sex, n (%) female	1,164 (50.9)	86,469 (58.9)
Ethnicity, n (%) western European	1,654 (93.1)	143,886 (98.0)
BMI, kg/ m²	25.6 ± 4.5	25.9 ± 4.2
Systolic blood pressure, mmHg	131 ± 14	125 ± 15
Diastolic blood pressure, mmHg	76 ± 8	74 ± 9
LDL-Cholesterol, mmol/L	2.67 ± 0.79	3.23 ± 0.91
Total Cholesterol, mmol/L	4.47 ± 0.90	5.08 ± 1.00
Smoking, n (%) yes
Current smoker count, n (%)	306 (13.6)	30,545 (20.8)
Former smoker count, n (%)	82 (3.6)	40,808 (27.8)
Never smoker count, n (%)	1,860 (82.8)	75,469 (51.4)
Cardiovascular disease, n (%) yes	108 (4.7)	3,344 (2.3)
Lipid lowering medication, n (%) yes	440 (19.9)	7,385 (5.0)
Antihypertensive medication, n (%) yes	384 (16.8)	16,182 (11.0)
Data are given as means (standard deviation) or medians with interquartilerange. Abbreviations: BMI = Body Mass Index, LDL = Low density lipoprotein.

Individuals with T1D were younger, had a comparable BMI, had a higher average blood pressure, with lower total and LDL-cholesterol in comparison to the background population. Moreover, individuals with T1D were less often current smokers in comparison to the background population, but more frequently had a positive medical history for CVD and were using more LLM and AHM (Table 1).

Figure 1 shows total and LDL-cholesterol levels per age group for both populations, stratified by medication use. In general, individuals with T1D had lower levels of LDL- and total-cholesterol in comparison to the background population. In those not using LLM, total and LDL-cholesterol gradually increased with higher age in the background population, whereas in the T1D population they remained relatively stable with increasing age. Amongst the individuals using LLM, LDL- and total-cholesterol levels decreased in the older age groups, and the differences between those with and without T1D were less pronounced.

When LDL-cholesterol levels are further stratified by sex, we still observed higher levels of LDL-cholesterol in the background population using LLM compared to those with T1D (Fig. 2). In males, the differences were more pronounced in the younger age groups. Moreover, there was a slight increase in LDL-cholesterol amongst older females with T1D, in comparison to their male counterparts. However, males using LLM appeared to have higher LDL-cholesterol values in the younger age groups in comparison to older age groups. Additionally, females using LLM appeared to have a less pronounced decline in LDL-cholesterol levels as age increased.

Different to the pattern observed for total and LDL-cholesterol, individuals with T1D had higher SBP compared to those without diabetes (Fig. 3). In the younger age groups not using AHM, the difference amounted up to 10 mmHg. In all cases, SBP levels increased with ageing. In those using AHM, the differences showed a similar pattern; however, there was more variation in the SBP in individuals with T1D. DBP was also higher amongst the younger age groups with T1D (18–30, 30–40, 40–50) while in the older age groups, these differences disappeared.

When the SBP is stratified by sex, we observed that the discrepancies in SBP between those with and without diabetes were most pronounced amongst young females, in comparison to their male counterparts (Fig. 4). Young females with T1D both with and without AHM use had higher SBP in comparison to the background population. In males, although the mean SBP was higher than in females, the SBP between those with and without diabetes was not as discrepant as observed among females.

Figure 5 shows the prevalence of CVD in the T1D and background population per age group. In this figure, we show that the prevalence of CVD was higher in the T1D population with the exception of the age group 18–30 years. The age group 40–50 years demonstrate the greatest prevalence ratio, 5.97, between the background and T1D population. Similar to the CVD prevalence, both AHM and LLM use was higher amongst the T1D population in comparison to the background population. The prevalence of medication use is further illustrated in supplementary Fig. 1.

In this study we have shown that concentrations of cholesterol and levels of blood pressure differ between individuals with T1D and people from the general population when compared across age decades and stratified for medication use. In particular, LDL-cholesterol values were lower, whilst SBP measurements were higher in almost all age groups irrespective of medication use. Furthermore, sex differences were present when comparing mean LDL and SBP between those with and without T1D. Despite a higher frequency of cardioprotective medication use amongst individuals withT1D, their prevalence of CVD was higher.

Lipid profile

Lipid metabolism is dependent on glycaemic control and serum insulin concentrations. The peripheral hyperinsulinaemia that accompanies exogenous insulin therapy in T1D leads to a reduction in VLDL production and an increase in LDL, leading to normal or lower concentrations of LDL-cholesterol amongst individuals with optimal glycaemic control (25). Analyses of individuals from the DCCT comparing T1D to healthy controls also showed similar lower LDL-cholesterol concentrations in young adults (26). We therefore expected that LDL-cholesterol may be comparable or lower in the T1D population in comparison to the background population. However, this probably only explains part of the low LDL-cholesterol concentrations we observed in the T1D population using LLM in our study.

Indication bias most likely contributed to the lower levels of LDL-cholesterol found in our T1D population. Individuals with T1D are more readily screened for dyslipidaemia and started on LLM. Presumably, the lower levels of LDL-cholesterol observed in the age groups above 40 without LLM use is a result of the individuals requiring LLM being prescribed it, thereby leaving the “healthiest” individuals in this group. What may support this is that the use of LLM was much higher in the population with T1D in comparison to the background population.

Additionally, the lower levels of LDL-cholesterol achieved in the T1D population using LLM could be a result of both treatment and better adherence driven by differences in motivation. Physicians prescribing LLM in individuals with T1D may do so more stringently by virtue of knowing the greater implication of cardiovascular risk for this population. Individuals with T1D may also be more motivated than the background population to protect themselves from cardiovascular complications. Studies have shown that individuals with more risk factors are more likely to be adherent to prescribed medications (27, 28). However, there is also likely a component of survival bias, whereby individuals with T1D and severe dyslipidaemia are lost earlier than individuals in the background population.

Interestingly, we also observed differences in LDL-cholesterol concentrations between males and females. LDL-cholesterol concentrations continued to decrease in the older age group of males not prescribed LLM, whereas in women this increased slightly. Previous studies have described lower use of cardioprotective medications despite guideline recommendations in women with T1D (29, 30). Our findings may point to potential differences in the management of lipid profiles between males and females. This is especially of importance as in contrast to the general population, females with T1D have a higher risk of vascular events in comparison to males (31).

Blood pressure

Previous studies have established the presence of accelerated vascular ageing and increased arterial stiffness in youth and young adults with T1D (32, 33). Early vascular changes may explain the increased SBPs measured across all age groups in our study. These findings are similar to a study in Finland, in which individuals with T1D were found to have higher SBP in comparison to controls (34). Moreover, the higher SBP measures in combination with lower DBP measured in the older age groups suggests that pulse pressure, an important marker of vascular stiffness and risk factor for CVD, also increases substantially (35).

Although the SBPs are lower in females compared to males, the discrepancy between individuals with T1D and the background population is largest amongst the females. This not only suggests a difference in management practices between males and females, but also suggests that women are less protected (29). In particular, the SBP measurements in females using AHM is much higher than males. This may further lead us to speculate that females are not being treated optimally in comparison to males.

Strengths and limitations

A major strength of this study includes the use of outpatient clinic data in a “real-world” setting and the comparison to individuals without diabetes in the Lifelines Cohort, a representative background population [34].

Although this study reflects the “real-world” setting, individuals at the extremes of lipid profiles and blood pressure measurements may be underrepresented. Individuals who historically have had good lipid profiles and blood pressure measurements may be screened less often. Whilst individuals who have lower drive for self-care may present less often in the outpatient clinic. Furthermore, individuals with severe complications such as renal failure are not captured in this population, as they are managed by nephrologists. This can contribute to an underestimation of the lipid and blood pressure measurements. Survival bias is also difficult to eliminate due to the cross-sectional nature of this study, therefore individuals with extreme measurements may already be lost at the time of measurement.

A further potential limitation is the quality and the completeness of the data extracted from electronic medical records (36). Nevertheless, only 4% of the T1D population were excluded in this study due to incomplete data.

Lastly, the use of out-patient clinic blood pressure measurements may lead to an overestimation in the measurements (37). Although guidelines recommend the use of two measurements when assessing blood pressure, this practice cannot be assured to the same degree as a study visit. Moreover, there were differences in fasting and non-fasting lipid measurements. However, studies have shown that total- and LDL-cholesterol remain stable in the fasted and non-fasted state (38).

Individuals with T1D continue to have an increased prevalence of CVD when compared to a background population. LDL-cholesterol and blood pressure are not comparable between individuals with T1D and the background population in most age groups. Although treated LDL-cholesterol levels appear to be better in individuals with T1D, the same cannot be said for blood pressure. Differences in the age group means between individuals with T1D and the background population appear to be the higher amongst females, potentially implying underlying sex differences in the management of LDL-cholesterol and blood pressure.

Type 1 diabetes (T1D), cardiovascular disease (CVD), type 2 diabetes (T2D), body mass index (BMI), lipid lowering medication (LLM), antihypertensive medication (AHM), systolic blood pressure (SBP), diastolic blood pressure (DBP).

Ethics approval and consent to participate

Diabeter/UMCG cohort

Lifelines cohort

The study protocol was approved by the medical ethical review committee of the University Medical Center Groningen, Groningen, the Netherlands.

Consent for publication

Not applicable

Availability of data and materials

The data of the T1D individuals that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

The manuscript is in part based on data from the Lifelines Cohort Study. Lifelines adheres to standards for data availability, and allows access for reproducibility of the study results. The data catalogue is publicly accessible at www.lifelines.nl. The dataset supporting the conclusions of this article is available through the Lifelines organization, and all international researchers can apply for data access at the Lifelines research office ([email protected]). For data access, a fee is required.

Competing interests

DM, and HJA are employed at Diabeter Netherlands, an independent clinic (owned by Medtronic), with brand-agnostic prescription under EU/Dutch healthcare laws. The research presented here was independently performed.

Funding

This study was supported by Dutch Diabetes Research Foundation grant no. 2015.16.1856, for which we are very grateful.

The Lifelines Biobank initiative has been made possible by subsidy from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG the Netherlands), University Groningen and the Northern Provinces of the Netherlands.

Authors' contributions

RV contributed to the design, analysis, interpretation and authored the manuscript. DM and HJA contributed to the design, acquisition of the data, and gave feedback on the manuscript. BW and MK contributed to the acquisition of the data and supervised the project.

Acknowledgements

The authors acknowledge the services of the Lifelines Cohort Study, the contributing research centres delivering data to Lifelines, and all the study participants.

Lind M, Svensson A-M, Kosiborod M, Gudbjörnsdottir S, Pivodic A, Wedel H, et al. Glycemic Control and Excess Mortality in Type 1 Diabetes. N Engl J Med. 2014;371(21):1972-82.
Livingstone SJ, Looker HC, Hothersall EJ, Wild SH, Lindsay RS, Chalmers J, et al. Risk of cardiovascular disease and total mortality in adults with type 1 diabetes: Scottish registry linkage study. PLoS medicine. 2012;9(10):e1001321-e.
Orchard TJ, Costacou T, Kretowski A, Nesto RW. Type 1 Diabetes and Coronary Artery Disease. Diabetes Care. 2006;29(11):2528-38.
Secrest AM, Becker DJ, Kelsey SF, LaPorte RE, Orchard TJ. All-cause mortality trends in a large population-based cohort with long-standing childhood-onset type 1 diabetes: the Allegheny County type 1 diabetes registry. Diabetes Care. 2010;33(12):2573-9.
Deckert T, Poulsen JE, Larsen M. Prognosis of diabetics with diabetes onset before the age of thirtyone. Diabetologia. 1978;14(6):363-70.
Miller RG, Mahajan HD, Costacou T, Sekikawa A, Anderson SJ, Orchard TJ. A Contemporary Estimate of Total Mortality and Cardiovascular Disease Risk in Young Adults With Type 1 Diabetes: The Pittsburgh Epidemiology of Diabetes Complications Study. Diabetes Care. 2016;39(12):2296-303.
The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977-86.
The Diabetes Control and Complications Trial Research Group. Intensive Diabetes Treatment and Cardiovascular Disease in Patients with Type 1 Diabetes. N Engl J Med. 2005;353(25):2643-53.
Committee ADAPP. 10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes—2022. Diabetes Care. 2021;45(Supplement_1):S144-S74.
Bebu I, Schade D, Braffett B, Kosiborod M, Lopes-Virella M, Soliman EZ, et al. Risk Factors for First and Subsequent CVD Events in Type 1 Diabetes: The DCCT/EDIC Study. Diabetes Care. 2020;43(4):867-74.
Nederlands Huisartsen Genootschap., Nederlandse Internisten Vereniging., Nederlandse Vereniging voor Cardiologie. Richtlijn Cardiovasculair risicomanagement (CVRM). 2018.
Rawshani A, Rawshani A, Franzén S, Eliasson B, Svensson A-M, Miftaraj M, et al. Mortality and Cardiovascular Disease in Type 1 and Type 2 Diabetes. N Engl J Med. 2017;376(15):1407-18.
Larsson SC, Wallin A, Håkansson N, Stackelberg O, Bäck M, Wolk A. Type 1 and type 2 diabetes mellitus and incidence of seven cardiovascular diseases. Int J Cardiol. 2018;262:66-70.
de Ferranti SD, de Boer IH, Fonseca V, Fox CS, Golden SH, Lavie CJ, et al. Type 1 Diabetes Mellitus and Cardiovascular Disease: A Scientific Statement From the American Heart Association and American Diabetes Association. Diabetes Care. 2014;37(10):2843-63.
Wadwa RP, Kinney GL, Maahs DM, Snell-Bergeon J, Hokanson JE, Garg SK, et al. Awareness and treatment of dyslipidemia in young adults with type 1 diabetes. Diabetes Care. 2005;28(5):1051-6.
Bradley TJ, Slorach C, Mahmud FH, Dunger DB, Deanfield J, Deda L, et al. Early changes in cardiovascular structure and function in adolescents with type 1 diabetes. Cardiovasc Diabetol. 2016;15:31.
Varkevisser RDM, Birnie E, Vollenbrock CE, Mul D, van Dijk PR, van der Klauw MM, et al. Cardiovascular risk management in people with type 1 diabetes: performance using three guidelines. BMJ Open Diabetes Res Care. 2022;10(4).
Stolk RP, Rosmalen JGM, Postma DS, de Boer RA, Navis G, Slaets JPJ, et al. Universal risk factors for multifactorial diseases. Eur J Epidemiol. 2008;23(1):67-74.
Scholtens S, Smidt N, Swertz MA, Bakker SJ, Dotinga A, Vonk JM, et al. Cohort Profile: LifeLines, a three-generation cohort study and biobank. Int J Epidemiol. 2015;44(4):1172-80.
R Core Team. R: A language and environment for statistical computing. Vienna, austria: R Foundation for Statistical Computing; 2021.
RStudio Team. RStudio: Integrated Development for R. PBC, Boston, MA RStudio; 2020.
Hadley Wickham, Romain François, Lionel Henry, Müller K. A Grammar of Data Manipulation. R package version 1.0.7 ed2021.
Wickham H. tidyr: Tidy Messy Data. R package version 1.1.3 ed2021.
DeWitt P. qwraps2: Quick Wraps 2. R package version 0.5.2 ed2021.
Verges B. Dyslipidemia in Type 1 Diabetes: AMaskedDanger. Trends Endocrinol Metab. 2020;31(6):422-34.
Schober E. Lipid and lipoprotein levels in young IDDM patients. Diabetes Care. 1993;16(4):664-5.
Hope HF, Binkley GM, Fenton S, Kitas GD, Verstappen SMM, Symmons DPM. Systematic review of the predictors of statin adherence for the primary prevention of cardiovascular disease. PLoS One. 2019;14(1):e0201196.
Leslie KH, McCowan C, Pell JP. Adherence to cardiovascular medication: a review of systematic reviews. J Public Health (Oxf). 2019;41(1):e84-e94.
Braffett BH, Bebu I, El Ghormli L, Cowie CC, Sivitz WI, Pop-Busui R, et al. Cardiometabolic Risk Factors and Incident Cardiovascular Disease Events in Women vs Men With Type 1 Diabetes. JAMA Netw Open. 2022;5(9):e2230710.
Larkin ME, Backlund JY, Cleary P, Bayless M, Schaefer B, Canady J, et al. Disparity in management of diabetes and coronary heart disease risk factors by sex in DCCT/EDIC. Diabet Med. 2010;27(4):451-8.
Huxley RR, Peters SA, Mishra GD, Woodward M. Risk of all-cause mortality and vascular events in women versus men with type 1 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2015;3(3):198-206.
van der Heyden JC, Birnie E, Mul D, Bovenberg S, Veeze HJ, Aanstoot HJ. Increased skin autofluorescence of children and adolescents with type 1 diabetes despite a well-controlled HbA1c: results from a cohort study. BMC Endocr Disord. 2016;16(1):49.
van der Heyden JC, Birnie E, Bovenberg SA, Cabezas MC, van der Meulen N, Mul D, et al. Do traditional cardiovascular risk factors solely explain intima-media thickening in youth with type 1 diabetes? J Diabetes Complications. 016;30(6):1137-43.
Rönnback M, Fagerudd J, Forsblom C, Pettersson-Fernholm K, Reunanen A, Groop P-H. Altered Age-Related Blood Pressure Pattern in Type 1 Diabetes. Circulation. 2004;110(9):1076-82.
Gordin D, Waden J, Forsblom C, Thorn L, Rosengard-Barlund M, Tolonen N, et al. Pulse pressure predicts incident cardiovascular disease but not diabetic nephropathy in patients with type 1 diabetes (The FinnDiane Study). Diabetes Care. 2011;34(4):886-91.
Dixon BE, Siegel JA, Oemig TV, Grannis SJ. Electronic health information quality challenges and interventions to improve public health surveillance data and practice. Public Health Rep. 2013;128(6):546-53.
Einstadter D, Bolen SD, Misak JE, Bar-Shain DS, Cebul RD. Association of Repeated Measurements With Blood Pressure Control in Primary Care. JAMA Intern Med. 2018;178(6):858-60.
Nordestgaard BG, Langsted A, Mora S, Kolovou G, Baum H, Bruckert E, et al. Fasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration cut-points-a joint consensus statement from the European Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine. =Eur Heart J. 2016;37(25):1944-58.

No competing interests reported.

Supplementaryfigure1.tiff
Supplementary figure 1 LLM and AHM medication use by age group.

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Differences in lipid and blood pressure measurements between individuals with type 1 diabetes and the general population

Status:

Version 1

Abstract

Background:

Methods:

Results:

Conclusion:

Figures

Background

Methods

Population

Type 1 Diabetes population: Diabeter/UMCG study

Control population: Lifelines Cohort study

Variables and definition

Statistical Analysis

Results

Discussion

Lipid profile

Blood pressure

Strengths and limitations

Conclusion

List Of Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Version 1