Abstract
Aim: Risk of cardiovascular disease is increased in patients with diabetes mellitus (DM). Cholesterol metabolism (hepatic synthesis and intestinal absorption) is known to be associated with cardiovascular risk. Next, we examined the association of DM with cholesterol absorption/synthesis.
Methods: The CACHE Consortium, which is comprised of 13 research groups in Japan possessing data of lathosterol (Latho, synthesis marker) and campesterol (Campe, absorption marker) measured by gas chromatography, compiled the clinical data using the REDCap system. Among the 3597 records, data from 2944 individuals were used for several analyses including this study.
Results: This study analyzed data from eligible 2182 individuals including 830 patients with DM; 42.2% were female, median age was 59 years, and median HbA1c of patients with DM was 7.0%. There was no difference in Latho between DM and non-DM individuals. Campe and Campe/Latho ratio were significantly lower in DM individuals than in non-DM individuals. When the associations of glycemic control markers with these markers were analyzed with multivariable-adjusted regression model using restricted cubic splines, Campe and Campe/Latho ratio showed inverse associations with glucose levels and HbA1c. However, Latho showed an inverted U-shaped association with plasma glucose, whereas Latho showed a U-shaped association with HbA1c. These associations remained even after excluding statin and/or ezetimibe users.
Conclusion: We demonstrated that DM and hyperglycemia were independent factors for lower cholesterol absorption marker levels regardless of statin/ezetimibe use.
Keywords: Lathosterol, Campesterol, Cholesterol metabolism, Diabetes, Glycemic control
See editorial vol. 30: 733-734
Introduction
Patients with diabetes are at an increased risk of cardiovascular events 1 - 3) . Haffner et al. 4) have reported that patients with type 2 diabetes mellitus (DM) who have no history of myocardial infarction have been reported to be at sufficiently high risk of coronary artery disease (CAD) as non-DM patients with a history of myocardial infarction. Since cardiovascular disease (CVD) is a major cause of mortality and morbidity in patients with DM, it is important to clarify the factors associated with atherosclerosis in DM.
DM is associated with metabolic disturbances such as hyperinsulinemia, insulin resistance, dyslipidemia, and obesity. Among these metabolic disorders, dyslipidemia, especially hypercholesterolemia, is one of the important factors for the development of CVDs in patients with DM.
Cholesterol in the body derives from cholesterol absorption from the intestinal tract and cholesterol synthesis in the liver. Cholesterol absorption has a multi-step process in which cholesterol is micellarized by bile acids in the intestinal lumen, taken up by enterocytes, assembled into lipoproteins, and transported to the lymphatic and blood circulation systems. Among them, Niemann–Pick C1-like 1(NPC1L1) protein has been identified as a transporter for cholesterol uptake at the surface of plasma membrane 5) . Moreover, ezetimibe is known as an inhibitor of NPC1L1 and is widely used as one of the cholesterol-lowering drugs for patients with hypercholesterolemia 6) . Cholesterol absorption can be assessed by measuring serum level of plant sterols such as campesterol (Campe), which are not synthesized in human but are absorbed through the NPC1L1 in the intestine. On the other hand, cholesterol synthesis is mainly conducted in the liver. It is well known that 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase is the rate-limiting enzyme in the cholesterol biosynthesis and statins selectively and strongly inhibit this enzyme and reduce serum cholesterol level 7) . Cholesterol synthesis can be assessed by measuring serum levels of cholesterol precursors such as lathosterol (Latho) 8) .
Previous studies have reported that high cholesterol absorption had higher risks for CVD in a prospective cohort 9) or case-control 10) studies, suggesting that high cholesterol absorption is a risk factor for CVD. In line with this, Lallys et al. 11) have reported that mRNA expression of Niemann–Pick C1-like 1 (NPC1L1) in the duodenum is higher in patients with type 2 DM than those in healthy subjects. Moreover, in the IMPROVE-IT study, the benefit of adding ezetimibe to statin on cardiovascular outcomes appeared to be enhanced among patients with DM 12) . Based on these studies, one may speculate that increased CVD risk in DM might be mediated by the increased cholesterol absorption. On the contrary, it has been reported that patients with type 2 DM, a high risk of CVD, have lower dietary cholesterol absorption efficiency 13 , 14) and higher cholesterol synthesis 14 - 18) than non-DM subjects. Thus, there is inconsistency in the link between DM and cholesterol absorption. To address this discrepant issue, further studies using large number of clinical data are needed to define the relationship between DM and cholesterol metabolism.
Accordingly, this study aimed to investigate the association of DM with serum markers of cholesterol metabolism. Additionally, we explored the possible associations of medications for DM with cholesterol metabolism markers.
Methods
Ethical Consideration
This study adhered to the latest version of Declaration of Helsinki and the Ethical Guidelines for Medical and Health Research Involving Human Subjects by Ministry of Health, Labor and Welfare and Ministry of Education, Japan (the original version in 2016 that was modified in 2017). The study protocol was reviewed and approved by the Ethics Committee, Osaka City University Graduate School of Medicine, Osaka, Japan (Approval No. 3871), and registered at University Hospital Medical Information Network Clinical Trial Registration (UMIN000030635) 19) . Moreover, the protocol of this study was approved by the review board of each participating institution prior to the study 19) .
Clinical Data Collection
A total of 13 research groups in Japan that possessed data of serum markers of cholesterol metabolism made up the CACHE (Cholesterol Absorption and Cholesterol synthesis in High-risk patients) consortium. Clinical data including serum biomarkers of cholesterol metabolism were collected and compiled using the web-based system called Research Electronic Data Capture (REDCap) 20 , 21) (https://projectredcap.org/about/) at Osaka City University (http://www.hosp.med.osaka-cu.ac.jp/self/hyokac/redcap/index.shtml).
Inclusion and Exclusion Criteria for the CACHE Study
This study had inclusion and exclusion criteria. The inclusion criteria were as follows: (1) patients at high risk of cardiovascular disease (coronary arterial disease, cerebrovascular disease, peripheral arterial disease, DM, chronic kidney disease including those treated with dialysis, familial hypercholesterolemia) or individuals who were examined for the screening of these conditions and (2) individuals whose cholesterol metabolism markers were already measured (serum Latho, Campe, and sitosterol levels). The exclusion criteria were as follows: participants and/or their family members who did not want to participate in this study upon information disclosure of this study.
Selection of the CACHE Population and Participants for this Analysis
From 3597 records accumulated in the REDCap system, we selected the CACHE population for analysis by excluding (1) the second records of the same individuals and (2) participants with missing values of age, sex, or both height and weight. For this “CACHE-DM analysis,” individuals were further excluded if diagnosis of DM was missing.
Definition of DM
DM was defined by either previous diagnosis of DM, use of any glucose-lowering medication, fasting plasma glucose (PG) of 126 mg/dL or higher, or hemoglobin A1c (HbA1c) values aligned by the National Glycohemoglobin Standardization Program (NGSP) of 6.5% or higher according to the diagnostic criteria by the American Diabetes Association and the Japan Diabetes Society 22 , 23) . In case JDS values had been recorded on REDCap, they were converted to the NGSP values using a formula previously published 24) . As glucose-lowering agents, we recorded the use of insulin, biguanide, thiazolidine, sulfonylurea, dipeptidyl peptidase-4 (DPP-4) inhibitors, α-glucosidase inhibitor (α-GI), glinides, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide 1 receptor antagonists.
Assays for Latho and Campe Concentrations
Serum concentrations of Latho and Campe were measured as the biomarkers for cholesterol synthesis and absorption, respectively, by gas chromatography at SRL Inc., Tokyo, Japan. The procedure of gas chromatography has been described elsewhere in detail 25) . In brief, 200 µL of serum was transferred to a glass tube, and the mixed solution of 2.5 mL of 1 M NaOH and ethanol was added. After cooling, 50 µL of 5α-cholestane (50 µg /mL) was added as an internal standard, and then 7.0 mL of n-hexane was added. After the mixing and centrifuging, 6 mL of n-hexane layer was fractionated and evaporated to dryness under a nitrogen stream. After drying, 100 µL of N,O-bis(trimethylsilyl)acetamide was added. After silylating the samples, the phase was evaporated to dryness under a nitrogen stream. A 0.6 mL of ethyl acetate was then added to the tube and mixed. To measure the noncholesterol sterols, 1.5 µL of the upper phase was injected to a gas chromatography machine (GC-2010) with flame ionization detector and manufactured by Shimadzu Corporation (Japan). GC-2010 gas chromatography data were used for image acquisition by a data processing device (C-R7A, Shimadzu Corporation, Japan). The concentration of each marker was determined by the internal standard curve method. In addition to concentrations of Latho and Campe, we calculated the Campe/Latho ratio for the assessment of relative status of cholesterol absorption to cholesterol synthesis 25) .
Other Variables
The CACHE study collected clinical data from medical records or datasets for research purpose regarding the following items: (1) clinical background including age, sex, smoking status, weight, height, high-risk conditions (prior CAD, prior stroke, prior peripheral artery disease, DM, chronic kidney disease (CKD) including dialysis, and familial hypercholesterolemia), and comorbidity such as hypertension and hyperuricemia; (2) blood tests including total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), PG, HbA1c, eGFR, uric acid (UA), serum albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and C-reactive protein (CRP); (3) physical examination and vital signs including height, body weight, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP); and (4) medication use including drugs for dyslipidemia, hypertension, and DM. eGFR was calculated from age, sex, and serum creatinine using the equation for the Japanese by Matsuo et al. 26) .
Hypertension was defined either by use of any anti-hypertensive medication, SBP of 140 mmHg or higher, or DBP of 90 mmHg or higher according to the criteria by the Japanese Society of Hypertension 27) .
CKD was defined in this study by eGFR lower than 60 mL/min/1.73m2 using the equation for the Japanese 26) . Since the CACHE study did not collect data on proteinuria, proteinuria was not considered for the definition of CKD in this study. Patients with kidney failure treated with hemodialysis were included in patients with CKD.
Familial hypercholesterolemia was diagnosed by the criteria from Japan Atherosclerosis Society 28) .
Regarding lipid parameters, the following rules were used: (1) Triglycerides (TG) and HDL-C values were used as entered. (2) Using total cholesterol (TC), TG, and HDL-C, non-HDL-C was calculated by subtracting HDL-C from TC, and LDL-C was calculated by the Friedewald formula 29) . (3) If LDL-C measured by a homogenous assay was entered but TC was not available, the LDL-C by a homogenous assay was used for analysis, and non-HDL-C was calculated as LDL-C plus TG/5. (4) If LDL-C or non-HDL-C cannot be calculated due to missing value of TG or HDL-C, it was handled as missing. In the CACHE-DM analysis, we presented data of TG, HDL-C, non-HDL-C, and LDL-C thus determined.
Statistical Analysis
The clinical characteristics were presented by DM or non-DM for the participants selected for this analysis. Continuous and categorical variables were summarized by medians (interquartile ranges) and numbers (percentages), respectively, and compared by Kruskal–Wallis test or Fisher’s exact test, respectively. The adjustment was made for age, sex, presence of any CVD [CAD, stroke, or peripheral artery disease (PAD)], hypertension, smoking status, CKD, dyslipidemia, BMI, UA, use of statin, use of fibrates, use of other lipid-lowering medications, and use of anti-hypertensive medications.
The associations of PG or HbA1c (exposure) with serum markers of cholesterol metabolism (outcomes) were examined using nonlinear regression models, which consider the restricted cubic spline term for PG or HbA1c with three knots (10th, 50th, and 90th percentile levels). The rms function was implemented in the rms and Hmisc packages in R. To meet the normal assumption of the regression model, we logarithmically transformed the objective variables and then used them in the regression models. In the above regression models, all missing values were complemented through the multiple imputation methods based on the predictive mean matching approach. Furthermore, to examine whether the associations differ depending on the patients’ characteristics, we also performed similar analyses considering a cross-product term between PG or HbA1c and each candidate, separately.
As additional analyses only in individuals without treatment with statin or ezetimibe, we examined the associations of serum lipid parameters (LDL-C, HDL-C, and TG) with the markers of cholesterol metabolism stratified by the presence of DM using nonlinear regression models adjusted for the same covariates as above.
All statistical inferences were conducted with two-sided 5% significance level using R software version 4.0.3 (https://cran.r-project.org/).
Results
Selection of Participants for this Analysis
Fig.1 shows the selection of participants for this CACHE-DM analysis. We collected 3,597 records for 2,989 individuals, and the repeated records were not used. By excluding 45 subjects with missing data on age, sex, or both height and weight, the CACHE population (N=2,944) was determined. For the purpose of this CACHE-DM analysis, 762 subjects were further excluded due to missing value of serum creatinine. A total of 2,182 individuals were selected for this analysis. Table 1 gives characteristics of the whole subjects for this analysis by DM status.
Fig.1. Selection of participants for this CACHE-DM analysis.
The initial dataset contained 3597 records from 2989 independent individuals. We excluded the second records from the same individuals. We further excluded 45 individuals who had missing values of age, sex, and/or both height and body weight. The remaining 2944 were defined as the CACHE population for further analyses including this CACHE-DM analysis. We excluded 762 individuals with missing history of diabetes, and the remaining 2182 subjects were analyzed in this CACHE-DM analysis.
Table 1. Characteristics of participants of this analysis.
| Variables | Unit | Total subjects | Missing | Non-DM | DM | P value |
|---|---|---|---|---|---|---|
| Number of subjects | --- | 2182 | --- | 1352 | 830 | --- |
| Age | years | 59 (49–67) | 0 (0.0) | 56 (45–64) | 63 (56–70) | ＜0.001 |
| Sex (women) | N (%) | 920 (42.2) | 0 (0.0) | 640 (47.3) | 280 (33.7) | ＜0.001 |
| Current smoking | N (%) | 458 (21.2) | 44 (1.0) | 265 (19.6) | 193 (23.9) | 0.02 |
| Body mass index (BMI) | kg/m2 | 22.7 (20.6–25.0) | 12 (0.5) | 22.0 (20.2–24.0) | 23.8 (21.7–25.0) | ＜0.001 |
| Systolic blood pressure (SBP) | mmHg | 130 (118–145) | 48 (2.2) | 128 (116–142) | 133 (121–147) | ＜0.001 |
| Diastolic blood pressure (DBP) | mmHg | 75 (68–81) | 48 (2.2) | 75 (68–81) | 74 (66–80) | 0.033 |
| Total cholesterol (TC) | mg/dL | 195 (165–225) | 189 (8.7) | 196 (167–227) | 193 (161–221) | 0.048 |
| LDL-cholesterol (LDL-C) | mg/dL | 114 (87–140) | 160 (7.3) | 113 (87–141) | 115 (87–140) | 0.826 |
| HDL-cholesterol (HDL-C) | mg/dL | 52 (42–65) | 46 (2.1) | 56 (46–69) | 46 (38–58) | ＜0.001 |
| Non-HDL-cholesterol (Non-HDL-C) | mg/dL | 141 (113–170) | 51 (2.3) | 139 (112–170) | 146 (115–172) | 0.045 |
| Triglyceride (TG) | mg/dL | 102 (72–145) | 46 (2.1) | 93 (66–129) | 118 (84–169) | ＜0.001 |
| Plasma glucose (PG) | mg/dL | 101 (92–118) | 568 (26) | 96 (89–103) | 127 (108–153) | ＜0.001 |
| HbA1c | % | 5.7 (5.4–6.5) | 343 (15.7) | 5.5 (5.2–5.7) | 7.0 (6.3–8.1) | ＜0.001 |
| eGFR | ＊ | 67.7 (5.1–82.7) | 260 (11.9) | 70.4 (4.7–84.3) | 60.7 (6.5–79.2) | 0.002 |
| Uric acid (UA) | mg/dL | 6.1 (4.8–7.3) | 582 (26.7) | 6.0 (4.6–7.2) | 6.4 (5.3–7.6) | ＜0.001 |
| Aspartate aminotransferase (AST) | U/L | 21 (18–26) | 872 (40.0) | 21 (18–25) | 22 (18–26) | 0.032 |
| Alanine aminotransferase (ALT) | U/L | 18 (14–27) | 872 (40.0) | 18 (13–25) | 21 (15–33) | ＜0.001 |
| Serum albumin | mg/dL | 4.2 (3.8–4.5) | 901 (41.3) | 4.2 (3.8–4.5) | 3.9 (3.6–4.4) | ＜0.001 |
| C-reactive protein (CRP) | mg/dL | 0.06 (0.03–0.18) | 917 (42.0) | 0.06 (0.03–0.16) | 0.08 (0.04–0.26) | ＜0.001 |
| Comorbidities | ||||||
| coronary artery disease (CAD) | N (%) | 278 (12.7) | 0 (0.0) | 105 (7.8) | 173 (20.8) | ＜0.001 |
| Stroke | N (%) | 104 (4.8) | 0 (0.0) | 50 (3.7) | 54 (6.5) | 0.003 |
| peripheral artery disease (PAD) | N (%) | 72 (3.3) | 0 (0.0) | 24 (1.8) | 48 (5.8) | ＜0.001 |
| Any cardiovascular disease (CVD) | N (%) | 385 (17.6) | 0 (0.0%) | 159 (11.8) | 226 (27.2) | ＜0.001 |
| Hypertension | N (%) | 1109 (51.5) | 54 (1.2) | 568 (42.8) | 541 (65.3) | ＜0.001 |
| Hyperlipidemia | N (%) | 1141 (53.5) | 102 (2.3) | 649 (48.2) | 492 (62.8) | ＜0.001 |
| Familial hypercholesterolemia (FH) | N (%) | 137 (6.3) | 0 (0.0) | 122 (9.0) | 15 (1.8) | ＜0.001 |
| chronic kidney disease (CKD) | N (%) | 777 (40.4) | 520 (11.9) | 474 (36.4) | 303 (48.9) | ＜0.001 |
| Lipid-lowering medications | N (%) | 388 (17.8) | 0 (0.0) | 175 (12.9) | 213 (25.7) | ＜0.001 |
| Statin | N (%) | 365 (16.7) | 0 (0.0) | 161 (11.9) | 204 (24.6) | ＜0.001 |
| Fibrate | N (%) | 13 (0.6) | 0 (0.0) | 6 (0.4) | 7 (0.8) | 0.239 |
| Ezetimibe | N (%) | 48 (2.2) | 0 (0.0) | 40 (3.0) | 8 (1.0) | 0.002 |
| Resins | N (%) | 12 (0.5) | 0 (0.0) | 10 (0.7) | 2 (0.2) | 0.126 |
| Probucol | N (%) | 15 (0.7) | 0 (0.0) | 13 (1.0) | 2 (0.2) | 0.048 |
| Omega-3 PUFA | N (%) | 26 (1.2) | 0 (0.0) | 13 (1.0) | 13 (1.6) | 0.206 |
| Nicotinic acid | N (%) | 3 (0.1) | 0 (0.0) | 1 (0.1) | 2 (0.2) | 0.307 |
| PCSK9 inhibitor | N (%) | 2 (0.1) | 0 (0.0) | 1 (0.1) | 1 (0.1) | 0.727 |
| Anti-hypertensive medications | N (%) | 716 (32.8) | 0 (0.0) | 354 (26.2) | 362 (43.6) | ＜0.001 |
| Anti-diabetes medications | N (%) | 716 (32.8) | 0 (0.0) | 0 (0.0) | 362 (43.6) | ＜0.001 |
| Sulfonylureas | N (%) | 111 (5.1) | 0 (0.0) | 0 (0.0) | 111 (13.4) | ＜0.001 |
| Glinide | N (%) | 13 (0.6) | 0 (0.0) | 0 (0.0) | 13 (1.6) | ＜0.001 |
| α-glucosidase inhibitors | N (%) | 120 (5.5) | 0 (0.0) | 0 (0.0) | 120 (14.5) | ＜0.001 |
| Biguanides | N (%) | 55 (2.5) | 0 (0.0) | 0 (0.0) | 55 (6.6) | ＜0.001 |
| Thiazolidine | N (%) | 65 (3.0) | 0 (0.0) | 0 (0.0) | 65 (7.8) | ＜0.001 |
| DPP-4 inhibitors | N (%) | 37 (1.7) | 0 (0.0) | 0 (0.0) | 37 (4.5) | ＜0.001 |
| SGLT2 inhibitors | N (%) | 1 (0.0) | 0 (0.0) | 0 (0.0) | 1 (0.1) | 0.202 |
| Insulin | N (%) | 141 (6.5) | 0 (0.0) | 0 (0.0) | 141 (17.0) | ＜0.001 |
The table gives medians (interquartile ranges) for continuous variables and numbers (percentages) for categorical variables.
Abbreviations: DM, diabetes mellitus; LDL, low-density lipoprotein; HDL-C, high-density lipoprotein; eGFR, estimated glomerular filtration rate; PUFA, polyunsaturated fatty acid; PCSK9, Proprotein convertase subtilisin/kexin type 9; DPP-4, dipeptidyl peptidase-4; SGLT2, sodium glucose cotransporter 2. ＊: mL/min/1.73m2
Comparison of the of Cholesterol Metabolism between DM and Non-DM
The levels of serum Latho, Campe, and Campe/Latho ratio were compared between DM and non-DM with covariates’ adjustment. Although there was no significant difference in Latho level between the two groups, Campe level and Campe/Latho ratio were significantly lower in the DM group ( Fig.2 ) . Moreover, the results were similar when we excluded patients who were on the use of statins and/or ezetimibe ( Supplemental Fig.1 ) .
Fig.2. Comparison of cholesterol metabolism biomarkers between non-DM and DM.
Data were adjusted for the following factors: age, sex, prior CVD (CAD, stroke, or PAD), hypertension, smoking, CKD, dyslipidemia, BMI, UA, use of statin, use of ezetimibe, use of other medications of dyslipidemia, and use of anti-hypertensive medications.
Supplemental Fig.1. Comparison of cholesterol metabolism biomarkers between non-DM and DM in subjects excluding those on the use of statins and/or ezetimibe.
Data were adjusted for the following factors: age, sex, prior CVD (CAD, stroke, or PAD), hypertension, smoking, CKD, dyslipidemia, BMI, UA, use of other medications of dyslipidemia, and use of anti-hypertensive medications.
Association between PG and Cholesterol Metabolism Biomarkers
Restricted cubic spline curves were used to show the multivariable-adjusted relationship between PG and serum levels of Latho, Campe, and Campe/Latho ratio in the whole subjects ( Fig.3 ) . Latho showed an inverted U-shaped association with PG, whereas Campe and Campe/Latho ratio had inverse associations with PG. Moreover, the results were similar in subjects after excluding statin and/or ezetimibe users ( Supplemental Fig.2A ) .
Fig.3. Association between PG and cholesterol metabolism biomarkers.
Data were shown in cubic spline curve adjusted for the following factors: age, sex, prior CVD (CAD, stroke, or PAD), hypertension, smoking, CKD, dyslipidemia, BMI, UA, use of statin, use of ezetimibe, use of other medications of dyslipidemia, and use of anti-hypertensive medications.
Supplemental Fig.2. Association between PG and cholesterol metabolism biomarkers and effect modification by sex, presence of CKD, or higher BMI (≥ 25 kg/m2) in subjects without the use of statins and/or ezetimibe .
The association between PG and each marker of cholesterol metabolism was analyzed in subjects excluding those on the use of statins and/or ezetimibe (A), or subgroups of sex (B), presence of CKD (C), or higher BMI (≥ 25 kg/m2) (D), and the effect modification of these factors was evaluated. The curves and shaded areas indicate means and 95% confidence intervals. Data were adjusted for the following factors: age, sex, prior CVD (CAD, stroke, or PAD), hypertension, smoking, CKD, dyslipidemia, BMI, UA, use of other medications of dyslipidemia, and use of anti-hypertensive medications.
The associations of PG and the markers of cholesterol metabolism were further investigated in analysis stratified by sex (female vs. male), CKD (presence vs. absence), use of statin (use vs. nonuse), or BMI (＜25 kg/m2 vs. ≥ 25 kg/m2) ( Fig.4 ) . There was no significant interaction between PG and any of these four factors. We noted the following findings in these stratified analyses: (1) male subjects had a higher Latho, lower Campe, and lower Campe/Latho ratio than female ones particularly in PG range below 120–130 mg/dL; (2) the presence of CKD was associated with lower Latho and higher Campe/Latho; (3) use of statin was associated with lower Latho, higher Campe, and higher Campe/Latho ratio; and (4) higher BMI was associated with higher Latho, lower Campe, and lower Campe/Latho ratio.
Fig.4. Effect modification by sex, CKD, use of statin, and BMI on the association between PG and cholesterol metabolism biomarkers.
The association between PG and each marker of cholesterol metabolism was analyzed in the subgroups of sex (A), presence of CKD (B), use of statin (C), or higher BMI (≥ 25 kg/m2) (D), and the effect modification of these factors was evaluated. The curves and shaded areas indicate means and 95% confidence intervals.
All these results in stratified analyses were similar when excluding statin and/or ezetimibe users ( Supplemental Fig.2B–2D ) .
Association between HbA1c and Cholesterol Metabolism Biomarkers
Restricted cubic spline curves were used to show the multivariable-adjusted relationship between HbA1c and serum levels of Latho, Campe, and Campe/Latho ratio in the whole subjects ( Fig.5 ) . Latho showed a U-shaped association with HbA1c, and Campe and Campe/Latho ratio had inverse associations with HbA1c. Moreover, the result was similar when we excluded subjects using statins and/or ezetimibe ( Supplemental Fig.3A ) .
Fig.5. Association between HbA1c and cholesterol metabolism biomarkers.
Data were shown in cubic spline curve adjusted for the following factors: age, sex, prior CVD (CAD, stroke, or PAD), hypertension, smoking, CKD, dyslipidemia, BMI, UA, use of statin, use of ezetimibe, use of other medications of dyslipidemia, and use of anti-hypertensive medications.
Supplemental Fig.3. Association between HbA1c and cholesterol metabolism biomarkers and effect modification by sex, presence of CKD, or higher BMI (≥ 25 kg/m2) in subjects without the use of statins and/or ezetimibe .
The association between HbA1c and each marker of cholesterol metabolism was analyzed in subjects excluding those on the use of statins and/or ezetimibe (A), or subgroups of sex (B), presence of CKD (C), or higher BMI (≥ 25 kg/m2) (D), and the effect modification of these factors was evaluated. The curves and shaded areas indicate means and 95% confidence intervals. Data were adjusted for the following factors: age, sex, prior CVD (CAD, stroke, or PAD), hypertension, smoking, CKD, dyslipidemia, BMI, UA, use of other medications of dyslipidemia, and use of anti-hypertensive medications.
The associations of HbA1c and the markers of cholesterol metabolism were further investigated in analysis stratified by sex (female vs. male), CKD (presence vs. absence), use of statin (use vs. nonuse), or BMI (＜25 kg/m2 vs. ≥ 25 kg/m2) ( Fig.6 ) . There was no significant interaction between HbA1c and any of these four factors. We noted the following findings in these stratified analyses: (1) Male subjects had a higher Latho, lower Campe, and lower Campe/Latho ratio than female ones particularly in HbA1c range below 6%–7%; (2) the presence of CKD was associated with lower Latho and higher Campe/Latho; (3) use of statin was associated with lower Latho, higher Campe, and higher Campe/Latho ratio; and (4) higher BMI was associated with higher Latho, lower Campe, and lower Campe/Latho ratio. All these results in stratified analyses were similar when we excluded patients who were treated with statins and/or ezetimibe ( Supplemental Fig.3B–3D ) .
Fig.6. Effect modification by sex, CKD, use of statin, and BMI on the association between HbA1c and cholesterol metabolism biomarkers.
The association between HbA1c and each marker of cholesterol metabolism was analyzed in the subgroups of sex (A), presence of CKD (B), use of statin (C), or higher BMI (≥ 25 kg/m2) (D), and the effect modification of these factors was evaluated. The curves and shaded areas indicate means and 95% confidence intervals.
Relationship between Glucose-Lowering Agents and Cholesterol Metabolism in Patients with DM
Finally, the levels of serum Latho, Campe, and Campe/Latho ratio were compared between the subjects with and without glucose-lowering agents with covariates’ adjustment in patients with DM ( Fig.7 ) . The use of insulin ( Fig.7A ) , biguanides ( Fig.7B ) , and DPP-4 inhibitor ( Fig.7C ) had no significant association with the markers of cholesterol metabolism. The level of Latho was lower in the subjects with thiazolidine than those without thiazolidine ( Fig.7D ) . Although the level of Campe was lower in the subjects with sulfonylurea than those without sulfonylurea, Campe/Latho ratio did not differ between the two groups ( Fig.7E ) . The subjects with α-GI had higher Latho level and higher Campe level and Campe/Latho ratio than those without α-GI ( Fig.7F ) .
Fig.7. Comparison of cholesterol metabolism biomarkers between subjects with or without various anti-diabetes agents.
Each marker of cholesterol metabolism was analyzed in the subgroups of use and nonuse of insulin (A), biguanide (B), DPP-4 inhibitor (C), thiazolidine (D), sulfonylurea (E), or α-GI (F). Data were adjusted for the following factors: age, sex, prior CVD (CAD, stroke, or PAD), hypertension, smoking, CKD, dyslipidemia, BMI, UA, use of statin, use of ezetimibe, use of other medications of dyslipidemia, and use of anti-hypertensive medications.
Associations of Serum Lipid Parameters with the Markers of Cholesterol Metabolism
In an additional analysis, we examined the associations of serum lipid parameters with the markers of cholesterol metabolism only in individuals without statin or ezetimibe treatment ( Supplemental Fig.4 ) . Latho levels showed positive associations with LDL-C, HDL-C, and TG levels in both DM and non-DM subgroups. Similarly, Campe levels showed positive associations with LDL-C, HDL-C, and TG levels in both DM and non-DM subgroups. Campe/Latho ratio showed almost positive associations with LDL-C and HDL-C, whereas the ratio was lower in higher TG levels. In these analyses, the presence DM was significantly associated with lower levels of Campe regardless of LDL-C, HDL-C, and TG levels.
Supplemental Fig.4. Association between serum lipid levels and cholesterol metabolism biomarkers in DM or non-DM subjects without the use of statins and/or ezetimibe.
The association between serum lipid levels (LDL-C, HDL-C, TG) and each marker of cholesterol metabolism (A: Latho, B: Campe, C: Campe/Latho ratio) were analyzed in subjects excluding those on the use of statins and/or ezetimibe, and the effect modification of DM was evaluated. The curves and shaded areas indicate means and 95% confidence intervals. Data were adjusted for the following factors: age, sex, prior CVD (CAD, stroke, or PAD), hypertension, smoking, CKD, dyslipidemia, BMI, UA, use of other medications for dyslipidemia, and use of anti-hypertensive medications, LDL-C, HDL-C, and TG.
Discussion
The CACHE-DM analysis demonstrated that the Campe level and Campe/Latho ratio were significantly lower in the DM group than in the non-DM group. This was confirmed in additional analysis excluding statin and/or ezetimibe users regardless of LDL-C, HDL-C, and TG levels. Moreover, PG and HbA1c were associated with Latho by inverting U-shape and U-shape, respectively, and negatively associated with Campe and Campe/Latho ratio. Furthermore, stratified analyses showed that these associations of these glycemic parameters with the markers of cholesterol metabolism were not significantly modified by sex, the presence of CKD, the use of statin, and the presence of obesity. The use of some glucose-lowering agents also had associations with the cholesterol metabolism markers.
Decreased cholesterol absorption in DM has been previously reported 13 , 14) . Although it was a report of a small number of cases, Gylling et al. 13) have demonstrated that serum Campe and sitosterol were lower in subjects with non-obese type 2 DM than in those without DM. They further demonstrated decreased cholesterol absorption by kinetics studies using radio-labeled tracers. They also reported similar results on obese type 2 DM 14) . In line with these reports, our results clearly showed that DM was significantly associated with lower Campe and the Campe/Latho ratio. These associations remained after excluding statin and/or ezetimibe users. Interestingly, the Campe and Campe/Latho ratio were inversely correlated with PG and HbA1c. Moreover, these associations remained significant even when stratified by sex, CKD, use of statin, and BMI. Regarding PG and cholesterol absorption, Cederberg et al. 30) have reported that Campe, sitosterol, and avenasterol, recognized as markers for cholesterol absorption, were inversely correlated with PG in individuals without DM. In contrast, cholesterol absorption in type 1 diabetes was reported to be increased 31) . A review article by Mashnafi et al. 32) clearly showed the quite different association with cholesterol metabolism between type 1 and 2 DM. Therefore, the type of DM may be an important factor affecting cholesterol absorption and synthesis.
Contrary to the abovementioned previous reports and our study results, it has been reported that mRNA expression of NPC1-L1 in the small intestine was increased, and mRNA expression of ATP-binding cassette (ABC) proteins G5 and G8, which were able to re-excrete some of the cholesterol and most of the plant sterols from the enterocyte back into the intestinal lumen, were decreased in type 2 DM 11) . There are a few more inconsistent reports on this topic. Two animal studies reported that NPC1L1 gene expression is upregulated in ZF rats 33) , whereas it was decreased in diabetic Psammomys obesus 34) . Moreover, according to the two cell culture experiments, NPC1L1 gene expression in cultured intestinal epithelial cells was upregulated in high-glucose conditions 35 , 36) . The apparent discrepancy between these studies suggests that mRNA expression of these transporters is not the only factor that determines the actual level of cholesterol absorption.
There is another discrepancy between this study and the previous one in cholesterol synthesis in patients with DM. Many previous studies have reported that cholesterol synthesis capacity was increased in DM 14 - 18) . Serum levels of Latho, a well-known marker of cholesterol synthesis, have been reported to be significantly increased in obese type 2 DM 15) . Moreover, it has been reported that the serum Latho level is higher in non-DM subjects with obesity than in normal subjects 37) and obesity was associated with higher Latho levels within subjects with type 2 DM patients as well 38 , 39) . However, Latho elevation was not observed in non-obese type 2 DM 14) . In this study, Latho in DM did not show at least a significant increase when comparing with that in non-DM, even after adjusting for the use of statins, which are well known as the inhibitor of cholesterol synthesis. The subjects with DM were not obese, as recognized by the average BMI, and the data were adjusted by BMI, so there may have been no difference in Latho. Thus, apparent discrepancy may be explained by the important role of obesity in increased cholesterol synthesis, BMI in the study subjects, and the statistical method used for analysis.
We noticed that PG and HbA1c were associated with serum Latho level in a different manner; that is, Latho showed an inverted U-shaped association with PG, whereas Latho showed a U-shaped association with HbA1c. Cederberg et al. reported that an inverted U-shaped association of Latho in PG has also been observed in the METSIM (a population-based cohort of Finnish men) study 30) . The inverted U-shaped association between Latho and PG was almost absent in the subjects with CKD (P for interaction =0.061) and high level of BMI (P for interaction =0.071), suggesting that CKD and obesity are potential modifiers of the association between PG and Latho levels. Interestingly, these two factors are known to cause insulin resistance. On the other hand, the U-shaped association between Latho and HbA1c was remained significant in any stratified analysis. Thus, although the exact mechanisms for the different associations of Latho with PG versus HbA1c remain unknown, it may be explained by chronicity of hyperglycemia and/or the effect of insulin resistance that was not assessed in this study. Clearly, further studies are needed.
To explore the possible influence of various glucose-lowering agents on cholesterol metabolism, we examined the associations of the use of each glucose-lowering agent with the markers of cholesterol metabolism. The result of α-GI was of particular interest. The use of α-GI was associated with a significantly lower serum level of Latho, and significantly higher serum level of Campe and Campe/Latho ratio, suggesting that the use of α-GI may affect cholesterol metabolism. Regarding the effect of α-GI on cholesterol metabolism, it was reported that miglitol treatment decreased LDL-C in subjects with metabolic syndrome 40) . Moreover, Iijima et al. 41) reported that, after treatment with miglitol for 12 months, serum level of Campe and sitosterol in T2DM was significantly increased and Latho tended to decrease. Taken together, our results may indicate the potential effects of α-GI on the metabolism of cholesterol among patients with DM.
On the other hand, our results demonstrated that serum level of Latho was lower in the subjects with the use of thiazolidine. Han et al. 42) reported that upregulation of peroxisome proliferator-activated receptor (PPAR)γ, which are activated by thiazolidine drugs, resulted in suppressed sterol regulatory element-binding protein 2 and HMG-CoA reductase gene expression in human liver cell line L02. This is a direct support of the interpretation that our result indicates the effect of thiazolidine on cholesterol metabolism. This notion is also indirectly supported by the relationship of a higher Latho level with obesity 37) and insulin resistance 43 - 45) . Therefore, it is possible that treatment with thiazolidine reduces serum Latho level through the improvement of insulin resistance by activation of PPARγ.
Moreover, in this study, serum level of Campe was lower in the subjects with the use of sulfonylurea. There are no reports of a relationship between sulfonylurea and cholesterol absorption, and the mechanism is also unknown. It is well known that sulfonylurea receptor, which belongs to the ABC protein superfamily, ABCC, plays important roles on the regulation of insulin secretion in pancreatic β cells 46) . On the other hand, ABCG5 and ABCG8, which are other ABC protein family, restrain cholesterol absorption in the lumen of the intestine by excreting absorbed cholesterol. As mentioned above, sulfonylurea receptor and ABCG5/8 are the same ABC protein family and the structure of these proteins is similar, it is possible that we speculate that sulfonylurea may affect ABCG5/8 through some mechanism. Clearly, further studies are needed.
Based on some of the previous studies 9 , 10) , we assumed that high cholesterol absorption is a risk factor of CVD and hypothesized that DM would have a positive association with Campe levels, which could be an additional mechanism for the increased CVD risk in patients with DM. As a result, the finding was opposite to our hypothesis. Importantly, however, this finding itself does not deny the CVD risk associated with high cholesterol absorption because we did not examine the association between cholesterol metabolism and CVD in this report. In addition, high-risk patients such as patients with DM do not necessarily have all risk factors for CVD. Thus, it is considered reasonable to assume that cholesterol absorption and DM are independent risk factors for CVD. In other words, it may be that a higher CVD risk in DM is not mediated by the changes in cholesterol absorption to a significant extent. In addition, results were inconsistent between studies using animals 33 , 34) and cultured cells 35 , 36) . Importantly, cholesterol absorption was quite different between type 1 and 2 diabetes in human studies 31) . Therefore, the observed lower levels of Campe in patients with mostly type 2 diabetes in this study were unexpected for us, but it is consistent with previous studies in type 2 diabetes.
This study has several limitations. First, we had no data regarding insulin resistance. Since it has been reported that cholesterol metabolism was influenced by insulin resistance 47) , additional data on insulin sensitivity will further clarify the relationship between cholesterol and glucose metabolism. Second, although this analysis included 2,182 individuals including patients using various glucose-lowering agents, only a small number of patients were treated with glinides, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide 1 receptor antagonists. Therefore, this study did not allow the analysis between these agents and cholesterol metabolism markers. Further studies are needed in patients treated with agents that were not analyzed in this study. Third, since information was lacking in the CACHE dataset, we could not assess the possible influence of the cholesterol metabolism markers on stage-classified microvascular complications, such as retinopathy, neuropathy, and nephropathy. Fourth, the CACHE population was collected from the experts of diabetology, endocrinology, cardiology, lipidology, nephrology, general internal medicine, and preventive medicine. Therefore, the population was heterogenous and included patients with various comorbidities and medications. To address this issue, we made aggressive statistical adjustments to the potential confounders and considered effect modifications by some important factors. Moreover, the results were confirmed by excluding statin and/or ezetimibe users. Fifth, we could not clearly classify the type of DM due to the insufficient information. Although we assume that most of the DM patients in this analysis were type 2 DM, we could not exclude the possibility that type 1 DM patients were included in those using insulin. On the other hand, the relatively large sample size was one of the strengths of this study compared to the previous studies. Sixth, we had relatively large numbers of missing values of PG and HbA1c that may be a limitation. However, this issue was carefully addressed by using multiple imputations as recommended 48) . Seventh, unfortunately, the meal status and the time of blood sampling were not uniform among the CACHE participants. Fasting blood were taken early in the morning in most of the subjects, whereas casual blood sampling was done in hemodialysis patients. Regarding diurnal variations in cholesterol metabolism, Schroor et al. 49) reviewed using meta-analysis that Latho had a diurnal rhythm, but the slope was seemed not to depend on the food intake. On the other hand, there was no significant slope of diurnal rhythm in Campe. Taken the previously reported knowledge into account, the effects of dietary status and time of sampling on the results might be present but not so large, if any.
Conclusion
In this study, we demonstrated that cholesterol absorption marker (Campe) levels were lower in subjects with DM than those without DM and there was no significant difference in cholesterol synthesis marker (Latho) levels between two groups. In addition, it is possible that cholesterol metabolism is dependent on glycemic control levels, which may be affected by some glucose-lowering medications used for T2DM.
Acknowledgements
Part of this study was presented at the 53rd Annual Meeting of the Japan Atherosclerosis Society (October 22-23, 2021, Kyoto, Hybrid version) and at the 18th International Symposium of Atherosclerosis (October 24-27, 2021, Kyoto, Hybrid version).
Funding
This study was supported by a grant to TS from Bayer Yakuhin Ltd (no specific grant number). The funder played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Conflict of Interest
Takeshi Matsumura reported personal fee from Eli Lily Japan KK, and Boehringer Ingelheim Japan Inc; and research grant from Shimazu Corporation. Yasushi Ishigaki reported personal fee from Bayer Yakuhin, Kowa Pharmaceutical Company, MSD, Novartis, Novo Nordisk, Ono Pharmaceutical, Sanofi K.K., and Takeda Pharmaceutica; research grant from Daiichi Sankyo, and Takeda Science Foundation; and Scholarship grant from MSD and Ono Pharmaceutical. Tomoko Nakagami reported personal fee from Sanwa Kagaku Kenkyusho Co Ltd, Novo Nordisk Pharma Ltd Japan, Eli Lily Japan KK, Sanofi K.K., Sumitomo Pharma, and Boehringer Ingelheim Japan Inc. Tatsuro Ishida reported personal fee from Bayer Yakuhin Ltd and Kowa Inc. Tetsuya Matoba reported personal fee from Bayer Yakuhin Ltd and MSD; and research grant from Amgen and Kowa. Shizuya Yamashita reported personal fee from Kowa. Hiroshi Yoshida reported personal fee from Denka Company Ltd and Kowa Company Ltd. Tetsuo Shoji reported personal fee and research grant from Bayer Yakuhin Ltd. Other authors reported no financial conflict of interest relevant to this study.
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