A comparison of different approaches to unravel the latent structure within metabolic syndrome

Volume changes of hippocampal and amygdala subfields in patients with mild cognitive impairment and Alzheimer's disease

BACKGROUND

Automated segmentation of hippocampal and amygdala subfields could improve classification accuracy of Mild Cognitive Impairments (MCI) and Alzheimer's Disease (AD) individuals.

METHODS

We applied T1-weighted magnetic resonance imaging (MRI) for 21 AD, 39 MCI and 32 normal control (NC) participants at 3-Tesla MRI. Twelve hippocampal subfields and 9 amygdala subfields in each hemisphere were analyzed using FreeSurfer 6.0.

RESULTS

Smaller volumes were observed in right/left whole hippocampus, right/left hippocampal tail, right/left subiculum, right Cornu ammonis 1(CA1), right/left molecular layer, right granule cell-molecular layer-dentate gyrus (GC-ML-DG), right CA4, right fimbria, right whole amygdala, right/left accessory basal, right anterior amygdala area, left central, left medial and right/left cortical nucleus of AD group compared to both MCI and NC groups (p < 0.001). The volumes of right presubiculum, right CA3, right hippocampus-amygdala-transition-area (HATA), right lateral, right basal, right central, right medial, right cortico-amygdaloid transition (CAT) and right paralaminar nucleus were significantly larger in NC than AD group (p ≤ 0.001), while the volumes of right subiculum, right CA1, right molecular layer, right whole hippocampus, right whole amygdala, right basal and right accessory basal were significantly larger in NC than MCI group (p ≤ 0.002). Trend analysis showed that most hippocampus and amygdala subfields have a trend of atrophy with the decline of cognitive function. Six core components were identified by the hierarchical clustering. The combined Receiver operating characteristic (ROC) analysis achieved the diagnostic performances (AUC: 0.81) in differentiating AD from MCI; (AUC: 0.79) in differentiating MCI from NC and (AUC: 0.97) in differentiating AD from NC.

CONCLUSIONS

Volumetric differences of hippocampus and amygdala were at a finer subfields scale, and the volumes of right basal nucleus, left parasubiculum, left medial nucleus, left GC-ML-DG, left hippocampal fissure, and right fimbria can be employed as neuroimaging biomarkers to assist the clinical diagnosis of MCI and AD.

Tackling hypo and hyper sensory processing heterogeneity in autism: From clinical stratification to genetic pathways

As an integral part of autism spectrum symptoms, sensory processing issues including both hypo and hyper sensory sensitivities. These sensory specificities may result from an excitation/inhibition imbalance with a poorly understood of their level of convergence with genetic alterations in GABA-ergic and glutamatergic pathways. In our study, we aimed to characterize the hypo/hyper-sensory profile among autistic individuals. We then explored its link with the burden of deleterious mutations in a subset of individuals with available whole-genome sequencing data. To characterize the hypo/hyper-sensory profile, the differential Short Sensory Profile (dSSP) was defined as a normalized and centralized hypo/hypersensitivity ratio from the Short Sensory Profile (SSP). Including 1136 participants (533 autistic individuals, 210 first-degree relatives, and 267 controls) from two independent study samples (PARIS and LEAP), we observed a statistically significant dSSP mean difference between autistic individuals and controls, driven mostly by a high dSSP variability, with an intermediated profile represented by relatives. Our genetic analysis tended to associate the dSSP and the hyposensitivity with mutations of the GABAergic pathway. The major limitation was the dSSP difficulty to discriminate subjects with a similar quantum of hypo- and hyper-sensory symptoms to those with no such symptoms, resulting both in a similar ratio score of 0. However, the dSSP could be a relevant clinical score, and combined with additional sensory descriptions, genetics and endophenotypic substrates, will improve the exploration of the underlying neurobiological mechanisms of sensory processing differences in autism spectrum.

Factor analyses of metabolic syndrome: A systematic review with special reference to Asian Indians

BACKGROUND

Metabolic syndrome is the constellation of cardiovascular disease risk factors and a growing public health issue affecting more than 20% of world population. Factor analysis is a powerful mathematical tool in exploring the underlying factors of any chronic diseases. Although it is most often criticized for its contrasting results for a common expression differently interpreted by the researchers yet fit the original data equally well.

OBJECTIVE

The present study aims to find out the underlying physiological domains for the phenotypic attribution of metabolic syndrome as documented in several studies.

METHODOLOGY

Literature search was done using Google Scholar, PUBMED, Research Gate and manual searching to identify relevant studies of the selected topic.

CONCLUSION

More than one physiological domain has been explored for the expression of metabolic syndrome explored in different studies. A reason for this disparity may be because most of explored factors are just mathematically significant but not biologically. Another reason may be the varied factor load concern. Therefore, a fixed factor load value is needed to be restricted for all studies across world.

Identification of Distinct Arterial Waveform Clusters and a Longitudinal Evaluation of Their Clinical Usefulness

Clustering of arterial blood pressure (BP) waveform parameters could summarize complex information into distinct elements, which could be used to investigate cumulative (nonredundant) associations. We investigated this hypothesis in a large, adult population-based study (ViDA trial [Vitamin D Assessment] trial). To interpret the clusters and evaluate their usefulness, we examined their predictors and associations with cardiovascular events. In 4253 adults (mean age 65 years; 55% male) without a prior cardiovascular event, suprasystolic oscillometry was performed, yielding aortic pressure waveforms and several hemodynamic parameters. Participants were followed up for 4.6 years (median), accruing 300 cardiovascular events. Principal component analysis reduced 14 arterial waveform parameters to 3 uncorrelated factors that together explained 90% of the variability of the original data. Factors 1, 2, and 3 appeared to represent BP pulsatility, mean BP, and wave reflection, respectively. Across 6 antihypertensive drug classes, there were no differences in brachial systolic (P=0.23) and diastolic (P=0.13) BP; but there were significant variations in factor 3 (P<0.0001), especially for β-blocker use. The first and third factors were positively associated with cardiovascular events (multivariable-adjusted standardized hazard ratio [95% CI]=1.33 [1.18-1.50] and 1.15 [1.02-1.30], respectively), whereas the second factor had a J-shaped relationship, with a nadir corresponding to a brachial diastolic BP of ≈75 mm Hg. In conclusion, BP pulsatility, mean BP, and wave reflection are prognostically meaningful, distinct aspects of arterial function that can be used to summarize physiological variations in multiple arterial waveform parameters and identify truly cumulative associations when used as cardiovascular risk outcomes.

Identifying clinical net benefit of psychotropic medication use with latent variable techniques: Evidence from Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD)

BACKGROUND

Poor medication adherence is common among individuals with Bipolar Disorder (BD). Understanding the sources of heterogeneity in clinical net benefit (CNB) and how it is related to psychotropic medications can provide new insight into ways to improve adherence.

METHODS

Data come from the baseline assessments of the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Latent class analysis identified groups of CNB, and validity of this construct was assessed using the SF-36. Adherence was defined as taking 75% or more of medications as prescribed. Associations between CNB and adherence were tested using multiple logistic regression adjusting for sociodemographic characteristics.

RESULTS

Five classes of CNB were identified: High (24%), Moderately high (12%), Moderate (26%), Moderately low (27%) and Low (12%). Adherence to psychotropic medications did not differ across classes (71% to 75%, χ²= 3.43, p = 0.488). Medication regimens differed by class: 57% of the High CNB were taking ≤2 medications, whereas 49% of the Low CNB were taking ≥4. CNB classes had good concordance with the SF-36.

LIMITATIONS

Missing data limited measures used to define CNB. Participants' perceptions of their illness and treatment were not assessed.

CONCLUSIONS

This novel operationalization of CNB has construct validity as indicated by the SF-36. Although CNB and polypharmacy regimens are heterogeneous in this sample, adherence is similar across CNB. Studying adherent individuals, despite suboptimal CNB, may provide novel insights into aspects influencing adherence.

Effect of Monthly, High-Dose, Long-Term Vitamin D Supplementation on Central Blood Pressure Parameters: A Randomized Controlled Trial Substudy

BACKGROUND

The effects of monthly, high-dose, long-term (≥1-year) vitamin D supplementation on central blood pressure (BP) parameters are unknown.

METHODS AND RESULTS

A total of 517 adults (58% male, aged 50-84 years) were recruited into a double-blinded, placebo-controlled trial substudy and randomized to receive, for 1.1 years (median; range: 0.9-1.5 years), either (1) vitamin D3 200 000 IU (initial dose) followed 1 month later by monthly 100 000-IU doses (n=256) or (2) placebo monthly (n=261). At baseline (n=517) and follow-up (n=380), suprasystolic oscillometry was undertaken, yielding aortic BP waveforms and hemodynamic parameters. Mean deseasonalized 25-hydroxyvitamin D increased from 66 nmol/L (SD: 24) at baseline to 122 nmol/L (SD: 42) at follow-up in the vitamin D group, with no change in the placebo group. Despite small, nonsignificant changes in hemodynamic parameters in the total sample (primary outcome), we observed consistently favorable changes among the 150 participants with vitamin D deficiency (<50 nmol/L) at baseline. In this subgroup, mean changes in the vitamin D group (n=71) versus placebo group (n=79) were -5.3 mm Hg (95% confidence interval [CI], -11.8 to 1.3) for brachial systolic BP (P=0.11), -2.8 mm Hg (95% CI, -6.2 to 0.7) for brachial diastolic BP (P=0.12), -7.5 mm Hg (95% CI, -14.4 to -0.6) for aortic systolic BP (P=0.03), -5.7 mm Hg (95% CI, -10.8 to -0.6) for augmentation index (P=0.03), -0.3 m/s (95% CI, -0.6 to -0.1) for pulse wave velocity (P=0.02), -8.6 mm Hg (95% CI, -15.4 to -1.9) for peak reservoir pressure (P=0.01), and -3.6 mm Hg (95% CI, -6.3 to -0.8) for backward pressure amplitude (P=0.01).

CONCLUSIONS

Monthly, high-dose, 1-year vitamin D supplementation lowered central BP parameters among adults with vitamin D deficiency but not in the total sample.

CLINICAL TRIAL REGISTRATION

URL: http://www.anzctr.org.au. Unique identifier: ACTRN12611000402943.

Metabolic syndrome is associated with an increased incidence of subclinical hypothyroidism - A Cohort Study

Prior cross-sectional analyses have demonstrated an association between subclinical hypothyroidism and metabolic syndrome and selected components. However, the temporal relation between metabolic syndrome and declining thyroid function remains unclear. In a prospective study, an unselected cohort of 66,822 participants with and without metabolic syndrome were followed. A proportional hazards regression model was used to estimate hazard ratios (HRs) and 95% CIs for hypothyroidism. Exploratory analyses for the relation between components of metabolic syndrome and declining thyroid function were also undertaken. During an average follow-up of 4.2 years, the incident rates for subclinical hypothyroidism were substantially higher in participants who began the study with metabolic syndrome compared with metabolically normal controls. After controlling for risk factors, patients with metabolic syndrome were at a 21% excess risk of developing subclinical hypothyroidism (adjusted HR 1.21; 95% CI 1.03–1.42). When individual components were analyzed, an increased risk of subclinical hypothyroidism was associated with high blood pressure (1.24; 1.04–1.48) and high serum triglycerides (1.18; 1.00–1.39), with a trend of increasing risk as participants had additional more components. Individuals with metabolic syndrome are at a greater risk for developing subclinical hypothyroidism, while its mechanisms and temporal consequences of this observation remain to be determined.

Ages at Onset of 5 Cardiometabolic Diseases Adjusting for Nonsusceptibility: Implications for the Pathogenesis of Metabolic Syndrome

To shed light on the etiology of metabolic syndrome development, it is important to understand whether its 5 component disorders follow certain onset sequences. To explore disease progression of the syndrome, we studied the ages at onset of 5 cardiometabolic diseases: abdominal obesity, diabetes, hypertension, hypertriglyceridemia, and hypo-α-lipoproteinemia. In analyzing longitudinal data from the Cardiovascular Disease Risk Factors Two-Township Study (1989-2002) in Taiwan, we adjusted for nonsusceptibility, utilizing the logistic-accelerated failure time location-scale mixture regression models for left-truncated and interval-censored data to simultaneously estimate the associations of township and sex with the susceptibility probability and the age-at-onset distribution of susceptible individuals for each disease. We then validated the onset sequences of 5 cardiometabolic diseases by comparing the overall probability density curves across township-sex strata. Visualization of these curves indicates that women tended to have onsets of abdominal obesity and hypo-α-lipoproteinemia in young adulthood, hypertension and hypertriglyceridemia in middle age, and diabetes later; men tended to have onsets of abdominal obesity, hypo-α-lipoproteinemia, and hypertriglyceridemia in young adulthood, hypertension in middle age, and diabetes later. Different onset patterns of abdominal obesity, hypo-α-lipoproteinemia, and male hypertension were identified between townships. Our proposed method provides a novel strategy for investigating both pathogenesis and preventive measures of complex syndromes.

siMS Score: Simple Method for Quantifying Metabolic Syndrome

Objective

To evaluate siMS score and siMS risk score, novel continuous metabolic syndrome scores as methods for quantification of metabolic status and risk.

Materials and Methods

Developed siMS score was calculated using formula: siMS score = 2*Waist/Height + Gly/5.6 + Tg/1.7 + TA_systolic/130—HDL/1.02 or 1.28 (for male or female subjects, respectively). siMS risk score was calculated using formula: siMS risk score = siMS score * age/45 or 50 (for male or female subjects, respectively) * family history of cardio/cerebro-vascular events (event = 1.2, no event = 1). A sample of 528 obese and non-obese participants was used to validate siMS score and siMS risk score. Scores calculated as sum of z-scores (each component of metabolic syndrome regressed with age and gender) and sum of scores derived from principal component analysis (PCA) were used for evaluation of siMS score. Variants were made by replacing glucose with HOMA in calculations. Framingham score was used for evaluation of siMS risk score.

Results

Correlation between siMS score with sum of z-scores and weighted sum of factors of PCA was high (r = 0.866 and r = 0.822, respectively). Correlation between siMS risk score and log transformed Framingham score was medium to high for age groups 18+,30+ and 35+ (0.835, 0.707 and 0.667, respectively).

Conclusions

siMS score and siMS risk score showed high correlation with more complex scores. Demonstrated accuracy together with superior simplicity and the ability to evaluate and follow-up individual patients makes siMS and siMS risk scores very convenient for use in clinical practice and research as well.

The association between Polycystic Ovary Syndrome (PCOS) and metabolic syndrome: a statistical modelling approach

OBJECTIVE

Polycystic ovary syndrome (PCOS) affects 12-21% of women. Women with PCOS exhibit clustering of metabolic features. We applied rigorous statistical methods to further understand the interplay between PCOS and metabolic features including insulin resistance, obesity and androgen status.

DESIGN

Retrospective cross-sectional analysis.

PATIENTS

Women with PCOS attending reproductive endocrine clinics in South Australia for the treatment of PCOS (n = 172). Women without PCOS (controls) in the same Australian region (n = 335) from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), a national population-based study (age- and BMI-matched within one standard deviation of the PCOS cohort).

MEASUREMENTS

The factor structure for metabolic syndrome for women with PCOS and control groups was examined, specifically, the contribution of individual factors to metabolic syndrome and the association of hyperandrogenism with other metabolic factors.

RESULTS

Women with PCOS demonstrated clustering of metabolic features that was not observed in the control group. Metabolic syndrome in the PCOS cohort was strongly represented by obesity (standardized factor loading = 0·95, P < 0·001) and insulin resistance factors (loading = 0·92, P < 0·001) and moderately by blood pressure (loading = 0·62, P < 0·001) and lipid factors (loading = 0·67, P = 0·002). On further analysis, the insulin resistance factor strongly correlated with the obesity (r = 0·70, P < 0·001) and lipid factors (r = 0·68, P < 0·001) and moderately with the blood pressure factor (loading = 0·43, P = 0·002). The hyperandrogenism factor was moderately correlated with the insulin resistance factor (r = 0·38, P < 0·003), but did not correlate with any other metabolic factors.

CONCLUSIONS

PCOS women are more likely to display metabolic clustering in comparison with age- and BMI-matched control women. Obesity and insulin resistance, but not androgens, are independently and most strongly associated with metabolic syndrome in PCOS.

Clinical relevance of cluster analysis in phenotyping allergic rhinitis in a real-life study

BACKGROUND

Disease stratification, using phenotypic characterization performed either by hypothesis- or data-driven methods, was developed to improve clinical decisions. However, cluster analysis has not been used for allergic rhinitis.

OBJECTIVE

To define clusters in allergic rhinitis and to compare them with ARIA (Allergic Rhinitis and Its Impact on Asthma), a hypothesis-driven approach.

METHODS

A French observational prospective multicenter study (EVEIL: Echelle visuelle analogique dans la rhinite allergique) was carried out on 990 patients consulting general practitioners for allergic rhinitis and treated as per clinical practice. In this study, changes in symptom scores, visual analogue scales and quality of life were measured at baseline and after 14 days of treatment. A post hoc analysis was performed to identify clusters of patients with allergic rhinitis – using Ward's hierarchical method – and to define their clinical relevance at baseline and after 14 days of treatment. The cluster approach was compared to the ARIA approach.

RESULTS

Patients were clustered into 4 phenotypes which partly followed the ARIA classes. These phenotypes differed in their disease severity including symptoms and quality of life. Physicians in real-life practice prescribed medication regardless of the phenotype and severity, with the exception of patients with ocular symptoms. Prescribed treatments were comparable in hypothesis- and data-driven analyses. The prevalence of uncontrolled patients during treatment was similar in the 4 clusters, but was significantly different according to the ARIA classes.

CONCLUSION

Cluster analysis using demographic and clinical parameters only does not appear to add relevant information for disease stratification in allergic rhinitis.

Prenatal exposure to cigarette smoke interacts with OPRM1 to modulate dietary preference for fat

BACKGROUND

Preference for fatty foods is a risk factor for obesity. It is a complex behaviour that involves the brain reward system and is regulated by genetic and environmental factors, such as the opioid receptor mu-1 gene (OPRM1) and prenatal exposure to maternal cigarette smoking (PEMCS). We examined whether OPRM1 and PEMCS interact in influencing fat intake and whether exposure-associated epigenetic modifications of OPRM1 may mediate this gene-environment interaction.

METHODS

We studied adolescents from a French Canadian genetic founder population, half of whom were exposed prenatally to maternal cigarette smoking. Fat intake was assessed with a 24-hour food recall in the form of a structured interview conducted by a trained nutritionist. The OPRM1 variant rs2281617 was genotyped for the whole sample with the Illumina Human610-Quad and HumanOmniExpress BeadChips. Methylation of blood DNA was assessed at 21 CpGs across OPRM1 in a subset of the sample using the Illumina HumanMethylation450 BeadChip.

RESULTS

We included 956 adolescents in our study. In the whole sample, OPRM1 (T carrier in rs2281617) was associated with lower fat intake (-1.6%, p = 0.017), and PEMCS was associated with higher fat intake (+1.6%, p = 0.005). OPRM1 and PEMCS interacted with each other (p = 0.003); the "protective" (fat intake-lowering) allele of OPRM1 was associated with lower fat intake in nonexposed (-3.2%, p < 0.001) but not in exposed individuals (+0.8%, p = 0.42). Further, PEMCS was associated with lower DNA methylation across multiple CpGs across OPRM1 in exposed versus nonexposed individuals (p = 0.031).

LIMITATIONS

A limitation of our study was its cross-sectional design.

CONCLUSION

Our study suggests that PEMCS may interact with OPRM1 in increasing fat preference. Silencing of the protective OPRM1 allele in exposed adolescents might be related to epigenetic modification of this gene.

Short and long-term lifestyle coaching approaches used to address diverse participant barriers to weight loss and physical activity adherence

Background

Individual barriers to weight loss and physical activity goals in the Diabetes Prevention Program, a randomized trial with 3.2 years average treatment duration, have not been previously reported. Evaluating barriers and the lifestyle coaching approaches used to improve adherence in a large, diverse participant cohort can inform dissemination efforts.

Methods

Lifestyle coaches documented barriers and approaches after each session (mean session attendance = 50.3 ± 21.8). Subjects were 1076 intensive lifestyle participants (mean age = 50.6 years; mean BMI = 33.9 kg/m²; 68% female, 48% non-Caucasian). Barriers and approaches used to improve adherence were ranked by the percentage of the cohort for whom they applied. Barrier groupings were also analyzed in relation to baseline demographic characteristics.

Results

Top weight loss barriers reported were problems with self-monitoring (58%); social cues (58%); holidays (54%); low activity (48%); and internal cues (thought/mood) (44%). Top activity barriers were holidays (51%); time management (50%); internal cues (30%); illness (29%), and motivation (26%). The percentage of the cohort having any type of barrier increased over the long-term intervention period. A majority of the weight loss barriers were significantly associated with younger age, greater obesity, and non-Caucasian race/ethnicity (p-values vary). Physical activity barriers, particularly thought and mood cues, social cues and time management, physical injury or illness and access/weather, were most significantly associated with being female and obese (p < 0.001 for all). Lifestyle coaches used problem-solving with most participants (≥75% short-term; > 90% long term) and regularly reviewed self-monitoring skills. More costly approaches were used infrequently during the first 16 sessions (≤10%) but increased over 3.2 years.

Conclusion

Behavioral problem solving approaches have short and long term dissemination potential for many kinds of participant barriers. Given minimal resources, increased attention to training lifestyle coaches in the consistent use of these approaches appears warranted.

The factor structure of the metabolic syndrome in obese individuals with binge eating disorder

OBJECTIVE

Metabolic syndrome (MetS) is strongly linked with cardiovascular disease and type-II diabetes, but there has been debate over which metabolic measures constitute MetS. Obese individuals with binge eating disorder (BED) are one of the high risk populations for developing MetS due to their excess weight and maladaptive eating patterns, yet, the clustering patterns of metabolic measures have not been examined in this patient group.

METHODS

347 adults (71.8% women) were recruited for treatment studies for obese individuals with BED. We used the VARCLUS procedure in the Statistical Analysis System (SAS) to investigate the clustering pattern of metabolic risk measures.

RESULTS

The analysis yielded four factors: obesity (body-mass-index [BMI] and waist circumference), lipids (HDL and triglycerides), blood pressure (systolic and diastolic blood pressure), and glucose regulation (fasting serum glucose and Hb1Ac). The four factors accounted for 84% of the total variances, and variances explained by each factor were not substantially different. There was no inter-correlation between the four factors. Subgroup analyses by sex and by race (Caucasian vs. African American) yielded the same four-factor structure.

CONCLUSION

The factor structure of MetS in obese individuals with BED is not different from those found in normative population studies. This factor structure may be applicable to the diverse population.

Clustering of the metabolic syndrome components in adolescence: role of visceral fat

Visceral fat (VF) promotes the development of metabolic syndrome (MetS), which emerges as early as in adolescence. The clustering of MetS components suggests shared etiologies, but these are largely unknown and may vary between males and females. Here, we investigated the latent structure of pre-clinical MetS in a community-based sample of 286 male and 312 female adolescents, assessing their abdominal adiposity (VF) directly with magnetic resonance imaging. Principal component analysis of the five MetS-defining variables (VF, blood pressure [BP], fasting serum triglycerides, HDL-cholesterol and glucose) identified two independent components in both males and females. The first component was sex-similar; it explained >30% of variance and was loaded by all but BP variables. The second component explained >20% of variance; it was loaded by BP similarly in both sexes but additional loading by metabolic variables was sex-specific. This sex-specificity was not detected in analyses that used waist circumference instead of VF. In adolescence, MetS-defining variables cluster into at least two sub-syndromes: (1) sex-similar metabolic abnormalities of obesity-induced insulin resistance and (2) sex-specific metabolic abnormalities associated with BP elevation. These results suggest that the etiology of MetS may involve more than one pathway and that some of the pathways may differ between males and females. Further, the sex-specific metabolic abnormalities associated with BP elevation suggest the need for sex-specific prevention and treatment strategies of MetS.

Heterogeneous behavior of lipids according to HbA1c levels undermines the plausibility of metabolic syndrome in type 1 diabetes: data from a nationwide multicenter survey

Background

Cardiovascular risk factors (CVRF) may cluster in type 1 diabetes, analogously to the metabolic syndrome described in type 2 diabetes. The threshold of HbA1_c above which lipid variables start changing behavior is unclear. This study aims to 1) assess the behavior of dyslipidemia according to HbA1_c values; 2) detect a threshold of HbA1_c beyond which lipids start to change and 3) compare the clustering of lipids and other non-lipid CVRF among strata of HbA1_c individuals with type 1 diabetes.

Methods

Effects of HbA1_c quintiles (1st: ≤7.4%; 2nd: 7.5-8.5%; 3rd: 8.6-9.6%; 4th: 9.7-11.3%; and 5th: >11.5%) and covariates (gender, BMI, blood pressure, insulin daily dose, lipids, statin use, diabetes duration) on dyslipidemia were studied in 1275 individuals from the Brazilian multi-centre type 1 diabetes study and 171 normal controls.

Results

Body size and blood pressure were not correlated to lipids and glycemic control. OR (99% CI) for high-LDL were 2.07 (1.21-3.54) and 2.51 (1.46-4.31), in the 4th and 5th HbA1_c quintiles, respectively. Hypertriglyceridemia increased in the 5th quintile of HbA1_c, OR 2.76 (1.20-6.37). OR of low-HDL-cholesterol were 0.48 (0.24-0.98) and 0.41 (0.19-0.85) in the 3rd and 4th HbA1_c quintiles, respectively. HDL-cholesterol correlated positively (0.437) with HbA1_c in the 3rd quintile. HDL-cholesterol and insulin dose correlated inversely in all levels of glycemic control.

Conclusions

Correlation of serum lipids with HbA1_c is heterogeneous across the spectrum of glycemic control in type 1 diabetes individuals. LDL-cholesterol and triglycerides worsened alongside HbA1_c with distinct thresholds. Association of lower HDL-cholesterol with higher daily insulin dose is consistent and it points out to a role of exogenous hyperinsulinemia in the pathophysiology of the CVRF clustering. These data suggest diverse pathophysiological processes depending on HbA1_c, refuting a unified explanation for cardiovascular risk in type 1 diabetes.