Starch intake, amylase gene copy number variation, plasma proteins, and risk of cardiovascular disease and mortality

The Malmö Diet and Cancer Study (MDCS) is a population-based prospective cohort study initiated to examine associations between diet and health outcomes [25]. It includes 30,446 individuals from Malmö, in the south of Sweden, aged 44–74 years at enrollment between 1991 and 1996. At baseline, all the MDCS participants underwent physical examinations, had blood taken, and answered questionnaires regarding lifestyle factors. Between October 1991 and February 1994, 6103 individuals was randomly selected to be studied for the epidemiology of carotid artery disease, and this sample is referred to as the Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC) [26]. All participants provided written informed consent. The study was approved by the Ethical Review Board at the Faculty of Medicine at Lund University, Sweden (LU 51/90), and was carried out in accordance with the Helsinki Declaration.

Among the 30,446 participants, we excluded those with missing information on dietary intake (n = 2213) and other relevant covariates (n = 563). We also excluded participants with a history of CVD (n = 829), diabetes (n = 1123), and cancer (n = 1574) at baseline, leaving 24,144 individuals. To reduce the bias of population stratification for the genetic analyses, we further excluded participants who were not born in Sweden or with unknown country of birth (n = 2876), leaving 21,268 individuals. A total of 20,264 individuals had available data on specific genetic variants within the AMY1 gene, and 3932 had information on AMY1 copy number.

Dietary intake was assessed using a modified diet history method consisting of (1) a 7-day food diary covering all cooked meals, cold beverages, and dietary supplements; (2) a 168-item food-frequency questionnaire (FFQ) for assessment of consumption frequencies and portion sizes of regularly eaten foods that were not covered by the 7-day food diary; and (3) an interview to ask for cooking methods and usual portion sizes for foods recorded in the food diary and to check for overlap between intakes reported by the food diary and the FFQ. Daily food intake was calculated by combing the intake data from the FFQ and the food diary. The energy and nutrient intake values were obtained from the MDCS Food and Nutrient Database that originated from PC KOST2-93, based on the Swedish National Food Agency. Starch intake was calculated by subtracting the intake of total sugars (i.e., mono- and disaccharides) from the total carbohydrate intake and was expressed as the percentage of total non-alcoholic energy intake (E%). The participants were divided into sex-specific quartiles based on their starch intake. Data on the validity [27, 28] and reproducibility [29] of the method have been published. The energy-adjusted Pearson correlation coefficients for carbohydrate intake were 0.66 and 0.70 for men and women, respectively.

AMY1 gene copy number was determined by droplet digital polymerase chain reaction (ddPCR) using the QX200 AutoDG ddPCR System (Bio-Rad Laboratories, Hercules, CA) following the manufacturer’s instructions, as previously described in detail by Rukh et al. [21]. Samples were analyzed along with negative control (RNase free water) on each plate. A high copy number control (NA18972; HapMap DNA: 17 copies of AMY1) and 2 samples from female donors with known AMY1 copy numbers (4 and 8 copies of AMY1) were included as controls in each run. Samples with high copy numbers were diluted and reanalyzed to exclude the possibility of the presence of too much DNA or a technical error. After amplification, the reactions were stored at 4 °C until the plates were read on a Bio-Rad QX200 Droplet Reader, and copy numbers were calculated through the use of the QuantaSoft software version 1.7.4 (Bio-Rad Laboratories). Copy number of AMY1 was divided into four groups: 1–4, 5–6, 7–9, and 10 and above copies.

We created AMY1-genetic risk scores (GRS) using 10 single nucleotide polymorphisms (SNPs) associated with copy number variation in AMY1 gene loci based on a previous publication of European data [30]. The relationship of these SNPs to AMY1 copy number was consistent across 2 independent cohorts of European-ancestry individuals sampled in the USA and Europe [30]. Individual SNP was coded as 0, 1, and 2 according to the number of increasing alleles. We calculated a weighted GRS using the following equation: AMY1-GRS = (β1 × SNP1 + β2 × SNP2 +…+ β3 × SNP3) × (n/sum of the β coefficients), where β is the β coefficient of each SNP for change in AMY1 copy number in the MDCS, and n was 10. The spearman correlation coefficient between AMY1 copy number and AMY1-GRS is 0.43 in our cohort. Details of SNPs and β coefficients of each SNP are presented in Additional file 1: Table S1.

For plasma proteins, the analysis was restricted to the MDC-CC participants. The available sample was 3680 and 3254 participants for identifying the proteins associated with starch intake and AMY1 copy number, respectively. Serum samples were stored frozen until proteomic profiling was performed using the Olink platform (Olink Proteomics, Uppsala, Sweden) (https://​www.​olink.​com/​content/​uploads/​2015/​12/​0696-v1.​3-Proseek-Multiplex-CVD-I-Validation-Data_​final.​pdf). A total of 92 plasma proteins were measured by the SciLifeLab with the Olink Proseek Multiplex proximity extension assays Cardiovascular. Values were expressed as normalized protein expression values as arbitrary units on a log2 scale. For statistical analysis, we excluded four proteins that were available in less than 75% of the individuals in the present study sample. Hence, 88 proteins were available for the analysis.

All participants were followed up from baseline examination to the diagnosis of CVD, emigration from Sweden, death, or December 31, 2018, whichever came first. Total CVD and mortality were obtained through the Swedish Hospital Discharge Register [31], the Swedish Cause of Death Register, and the Stroke Register of Malmö [32]. Total CVD was defined as a hospital admission or death using International Classification of Diseases (ICD) codes, including coronary heart disease (ICD-9 codes 410A-410X and ICD-10 code I21), death attributable to ischemic heart disease (ICD-9 codes: 410-414; ICD-10 codes: I20-I25), and ischemic stroke (ICD-9 code 434 and ICD-10 code I63). CVD mortality was defined as ICD-9 codes 390-459 and ICD-10 I00-I99.

Data on smoking habits (current, former, or never), educational level (the highest qualification attained), and heredity for diseases (including cancer, myocardial infarction, stroke, and diabetes) and other lifestyle factors were obtained using a structured questionnaire. Four separate heredity scores were constructed and participants were categorized as having low heredity (e.g., with no first-degree relatives with the disease), medium heredity (with one first-degree relative with the disease), and high heredity (with at least two first-degree relatives with the disease) for disease [33]. Alcohol consumption was divided into six categories. Zero consumers reported no consumption of alcohol in the 7-day food diary or during the previous year in the questionnaire. The other individuals were divided into sex-specific quintiles based on the reported alcohol intake from the 7-day food diary. Leisure-time physical activity was categorized into five predefined groups based on metabolic equivalent task (MET) hours per week: < 7.5, 7.5–15, 15–25, 25–50, and > 50 MET hour/week. BMI (kg/m²) was calculated as weight in kilograms divided by the square of the height in meters. Systolic and diastolic blood pressures were measured after 10 min of rest in the supine position. Hypertension was defined as systolic/diastolic blood pressures ≥ 140/90 mmHg and/or current use of antihypertensive medications. The variable for dietary assessment method was created based on the interview time of 60 min before September 1994 and 45 min from 1 September 1994. The season of diet data collection was defined as spring, summer, autumn, and winter. A diet quality index based on the Swedish dietary guidelines was calculated using the following factors: fiber (> 2.4 g/MJ), fruit and vegetables (> 400 g/day), fish (> 300 g/week), added sugar (< 10% energy), and red and processed meat (< 500 g/week). Each point was given for each favorable diet factor, with the total score ranging from 0 to 5 [34].

To evaluate differences in baseline characteristics for participants with different quartiles of starch intake, 𝜒2 test was conducted for categorical variables, and analysis of variance was conducted for continuous variables. Cox proportional hazards models were conducted with quartiles of sex-specific starch intake as the exposures of interest, incident CVD and mortality as the outcome, and time-to-event as the time metric. We tested for linear trends by using the ordinal score on quartiles of starch intake. Three models were applied in our analyses. Model 1 was adjusted for age, sex, dietary assessment method, season, and total energy intake. Model 2 was additionally adjusted for leisure-time physical activity, smoking status, alcohol consumption, education, heredity score, and diet quality index. Model 3 was further adjusted for hypertension and BMI. Interaction by cereal intake, bread intake, potato intake, and sex were performed by adding a multiplicative factor between these starch food source (quartiles) and starch (continues) in model 3.

Age- and sex-adjusted Cox proportional hazards models were used to examine the associations of AMY1 copy number and AMY1-GRS with risk of CVD and mortality. Interaction by age were performed by adding a multiplicative factor between age and AMY1 copy number in age- and sex-adjusted model. Furthermore, multiplicative interactions between AMY1 copy number or AMY1-GRS and starch intake were evaluated using the Wald test on cross-product terms into multivariable models (the above model 3). The association between starch intake and risk of CVD and mortality were evaluated in strata of AMY1 copy number (1–4 copies, 5–6 copies 7–9 copies, 10 and above copies) and AMY1-GRS (quintiles). We verified the assumption of linearity between starch intake, AMY1 copy number, AMY1-GRS, and risk of CVD and mortality using restricted cubic spline functions with the SAS macro written by Desquilbet and Mariotti [35].

Associations between starch intake and plasma proteins were studied with three multivariable linear regression models: model 1 adjusted for age, sex, season, and total energy intake, model 2 with further adjustment for education, smoking status, alcohol consumption, and leisure-time physical activity, and model 3 with additional adjustment for BMI. Associations between AMY1 copy number and plasma proteins were studied with multivariable linear regression, adjusted for age and sex. Tests of statistical significance were corrected for multiple comparisons using the Bonferroni method for 88 tests (P < 0.00057).

Cox proportional hazards models were used to study associations between proteins that were associated with starch intake or AMY1 copy number (after Bonferroni correction) and risks of CVD and mortality. Models 1 were adjusted for age and sex, and model 2 were further adjusted for physical activity, smoking status, alcohol consumption, educational level, and heredity scores. To test for the potential influence of obesity in the association between plasma protein and risk of CVD and mortality, we additionally adjusted this model for BMI (model 3). All tests were two-sided, and we considered P < 0.05 to be statistically significant. SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) was used for the analyses.