Association of preterm birth with lipid disorders in early adulthood: A Swedish cohort study.

BACKGROUND: Preterm birth has previously been linked with cardiovascular disease (CVD) in adulthood. However, associations with lipid disorders (e.g., high cholesterol or triglycerides), which are major risk factors for CVD, have seldom been examined and are conflicting. Clinicians will increasingly encounter adult survivors of preterm birth and will need to understand the long-term health sequelae. We conducted the first large population-based study to determine whether preterm birth is associated with an increased risk of lipid disorders. METHODS AND
FINDINGS: A retrospective national cohort study was conducted of all 2,235,012 persons born as singletons in Sweden during 1973 to 1995 (48.6% women), who were followed up for lipid disorders identified from nationwide inpatient, outpatient, and pharmacy data through 2016 (maximum age 44 years). Cox regression was used to adjust for other perinatal and maternal factors, and co-sibling analyses assessed the potential influence of unmeasured shared familial (genetic and/or environmental) factors. A total of 25,050 (1.1%) persons were identified with lipid disorders in 30.3 million person-years of follow-up. Each additional 5 weeks of gestation were associated with a 14% reduction in risk of lipid disorders (adjusted hazard ratio [HR], 0.86; 95% CI, 0.83-0.89; P < 0.001). Relative to full-term birth (gestational age 39-41 weeks), the adjusted HR associated with preterm birth (<37 weeks) was 1.23 (95% CI, 1.16-1.29; P < 0.001), and further stratified was 2.00 (1.41-2.85; P < 0.001) for extremely preterm (22-27 weeks), 1.33 (1.19-1.49; P < 0.001) for very preterm (28-33 weeks), and 1.19 (1.12-1.26; P < 0.001) for late preterm (34-36 weeks). These findings were similar in men and women (e.g., preterm versus full-term, men: HR, 1.22; 95% CI, 1.14-1.31; P < 0.001; women: HR, 1.23; 1.12-1.32; P < 0.001). Co-sibling analyses suggested that they were substantially though not completely explained by shared genetic or environmental factors in families. The main study limitation was the unavailability of laboratory data to assess specific types or severity of lipid disorders.
CONCLUSIONS: In this large national cohort, preterm birth was associated with an increased risk of lipid disorders in early- to midadulthood. Persons born prematurely may need early preventive evaluation and long-term monitoring for lipid disorders to reduce their future cardiovascular risks.

Introduction

Preterm birth (gestational age <37 weeks) affects 11% of all births worldwide [1], 10% in the United States [2, 3], and 5% to 8% in most European countries [4]. Preterm birth has previously been associated with an increased risk of ischemic heart disease (IHD) in adulthood [5]. The underlying mechanisms may involve higher risks of hypertension [6, 7] and diabetes [8-11] that also have been reported in preterm-born adults. However, the risks of lipid disorders (e.g., high cholesterol or triglycerides), which are also major risk factors for IHD, have seldom been examined. Such information could improve our understanding of potential mechanisms and further inform preventive actions and anticipatory screening in the growing population of adults who were born prematurely.

The few prior studies of preterm birth in relation to lipid disorders have yielded discrepant findings. The largest study to date assessed plasma lipid levels in a British cohort of 7,847 adults aged 44 to 45 years and reported a modest inverse linear association between gestational age at birth and total cholesterol levels among women only [12]. However, statistical power was limited for most comparisons, and risks in specific gestational age groups were not reported. The only other prior investigations involved small clinical samples of up to a few hundred participants. Some [13-15] but not all [16-19] of these studies reported higher levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), or triglycerides in adults born prematurely or with low birth weight. No larger population-based studies have examined the risk of lipid disorders in persons born prematurely who were followed into adulthood. Furthermore, if such associations exist, it is unknown whether they are related to shared familial (genetic and/or environmental) factors that predispose to both preterm birth and lipid disorders or direct effects of preterm birth.

To address these knowledge gaps, we conducted a national cohort study of more than 2 million adults in Sweden. The goals of this study were to examine associations between gestational age at birth and risk of lipid disorders at ages 18 to 44 years, the maximum follow-up currently possible in this large cohort, to assess whether these associations differ according to sex or fetal growth, and to explore for potential confounding by shared familial (genetic and/or environmental) factors using co-sibling analyses.

Methods

Study population

The Swedish Birth Registry contains prenatal and birth information for nearly all births nationwide since 1973 [20]. Using this registry, we identified all 2,341,668 singleton live births in Sweden during 1973 to 1995. These birth years were chosen to allow sufficient follow-up into adulthood. We excluded all 98,714 (4.2%) persons who were no longer living in Sweden at age 18 years, 1,162 (<0.1%) others who were diagnosed with lipid disorders prior to age 18 years (thus allowing assessment of new-onset lipid disorders in adulthood), and 6,780 (0.3%) others with missing information for gestational age, leaving 2,235,012 persons (95.4% of the original cohort) for inclusion in the study. This study was approved by the ethics committee of Lund University in Sweden (No. 2010/476). Participant consent was not required because this study used only de-identified registry-based secondary data.

Ascertainment of gestational age at birth and lipid disorders

Gestational age at birth was identified from the Swedish Birth Registry based on maternal report of last menstrual period in the 1970s and ultrasonography estimation starting in the 1980s and onward. This was analyzed alternatively as a continuous variable or categorical variable with 6 groups: extremely preterm (22–27 weeks), very preterm (28–33 weeks), late preterm (34–36 weeks), early term (37–38 weeks), full term (39–41 weeks, used as the reference group), and post term (≥42 weeks). Early-term birth was examined as a separate category because it has previously been associated with increased cardiovascular- and endocrine-related mortality relative to later-term birth [21, 22]. In addition, the first 3 groups were combined to provide summary estimates for preterm birth.

The study cohort was followed up for lipid disorders from age 18 years through the end of follow-up in 2016 (maximum age 44 years). Lipid disorders were defined based on either of the following: (1) International Classification of Diseases (ICD) codes for lipid disorders (ICD-8/9: 272; ICD-10: E78); or (2) prescription of lipid-modifying medications (Anatomical Therapeutic Chemical [ATC] Classification System code C10) without a concurrent diagnosis of IHD (ICD-8/9: 410–414; ICD-10: I20-I25; i.e., to exclude medications prescribed solely for secondary prevention of IHD). ICD codes were identified from all primary and secondary diagnoses in the Swedish Hospital and Outpatient Registries. The Swedish Hospital Registry contains all primary and secondary hospital discharge diagnoses from 6 populous counties in southern Sweden starting in 1964, and with nationwide coverage starting in 1987; these diagnoses are currently >99% complete, and their positive predictive value for most chronic disorders has been reported to be 85% to 95% [23]. The Swedish Outpatient Registry contains all outpatient diagnoses from specialty clinics nationwide starting in 2001. Lipid-modifying medication prescriptions were identified using the Swedish Pharmacy Registry, which includes all prescriptions nationwide since July 1, 2005.

Other study variables

Other perinatal and maternal characteristics that may be associated with gestational age at birth and lipid disorders were identified using the Swedish Birth Registry and national census data, which were linked using an anonymous personal identification number. The following were included as adjustment variables: birth year (continuous and categorical by decade), sex, birth order (1, 2, ≥3), maternal age at delivery (continuous), maternal education level (≤9, 10–12, >12 years), maternal birth country or region (Sweden, other Europe/US/Canada, Asia/Oceania, Africa, Latin America, other/unknown), maternal body mass index (BMI; continuous), and maternal history of lipid disorders (identified using the same ICD codes as above, and ICD-7 289.0). In a secondary analysis, further adjustment was made for fetal growth (small for gestational age [SGA; <10th percentile]; appropriate for gestational age [AGA; 10th–90th percentile]; large for gestational age [LGA; >90th percentile]), to assess the association between gestational age at birth and lipid disorders independently of fetal growth.

Maternal BMI was assessed at the beginning of prenatal care starting in 1982 and was available for 38.8% of women. Data were >99% complete for all other variables. Missing data for each covariate were imputed using a standard multiple imputation procedure based on the variable’s relationship with all other covariates and lipid disorders [24]. The analytic approach was determined prior to beginning the data analyses, though there was no formal statistical analysis plan.

Statistical analysis

Cox proportional hazards regression was used to compute hazard ratios (HRs) and 95% CIs for associations between gestational age at birth and lipid disorders at ages 18 to 44 years. Attained age was used as the Cox model time axis. Individuals were censored at emigration as determined by absence of a Swedish residential address in census data (n = 131,492; 5.9%) or death as identified in the Swedish Death Registry (n = 16,004; 0.7%). Analyses were conducted both unadjusted and adjusted for covariates (as above).

Co-sibling analyses were performed to assess for potential confounding by unmeasured shared familial (genetic and/or environmental) factors among individuals who had at least one sibling (N = 1,881,473 [84.2% of the cohort] in 846,164 families). This approach can help further elucidate whether associations observed in the primary analyses are related to direct effects of preterm birth as opposed to shared genetic or environmental factors that may predispose to both preterm birth and lipid disorders. These analyses used stratified Cox regression with a separate stratum for each family as identified by the mother’s anonymous identification number. In the stratified Cox model, each set of siblings had its own baseline hazard function that reflects the family’s shared genetic and environmental factors, and thus associations between gestational age at birth and time to diagnosis of lipid disorders were examined within families, controlling for their shared factors. In addition, these analyses were further adjusted for the same covariates as in the primary analyses.

The co-sibling design has certain limitations and assumptions [25-27] that were explored in 2 sensitivity analyses. First, by definition, this design includes only persons with siblings. We explored generalizability to persons without siblings by repeating the primary analyses while restricting alternatively to persons with siblings (N = 1,881,473) or those without (N = 353,539). In a post hoc meta-analysis, risk estimates from a standard analysis of persons without siblings and from the co-sibling analysis were pooled using the inverse-variance method [28]. Second, we explored the possibility of preterm birth in one sibling influencing the risk of lipid disorders in another (i.e., carryover effects) by fitting bidirectional models to explore whether different patterns of preterm birth within families (i.e., either the first- or second-born offspring had preterm birth) modified the co-sibling analysis results [26]. In these analyses, the co-sibling results from families in which the first child was born preterm (N = 43,031) were compared with those in which the second child was born preterm (N = 28,609).

Potential interactions between gestational age at birth and sex, fetal growth, or mode of delivery (vaginal or Caesarean section) were examined in relation to risk of lipid disorders on the additive and multiplicative scale [29, 30]. Several other sensitivity analyses also were performed: First, we explored the influence of lipid disorders that were potentially secondary to atherogenic medications. In these analyses, follow-up was started at July 1, 2006 (one year after inception of the pharmacy register). Lipid disorders were considered to be potentially secondary to medications and were excluded from the study outcome if preceded by ≥2 prescriptions of the following medications previously associated with lipid disorders: corticosteroids (ATC code H02), antiepileptics (N03), phenothiazines (N05AA, N05AB, N05AC), androgens (G03B), cyclosporine (L04AD01), and retinoids (D10BA) for systemic use [31]. Second, as an alternative to multiple imputation, the primary analyses were repeated after restricting to individuals with complete data (N = 866,362). Third, the primary analyses were repeated after restricting to persons born in 1982 or later when gestational ages were estimated predominantly by ultrasound rather than last menstrual period (N = 1,399,126). All statistical tests were 2-sided and used an α-level of 0.05. All analyses were conducted using Stata version 15.1 (StataCorp, https://www.stata.com).

Results

Table 1 shows perinatal and maternal characteristics by gestational age at birth. Preterm infants were more likely than full-term infants to be male or first-born; and their mothers were more likely to be at the extremes of age, have low education level, be foreign-born, or have a history of lipid disorders.

Table 1

Characteristics of study participants by gestational age at birth, Sweden, 1973–1995.

Characteristics	Extremely preterm(22–27 weeks)N = 1,900	Very preterm(28–33 weeks)N = 20,880	Late preterm(34–36 weeks)N = 83,692	Early term(37–38 weeks)N = 377,901	Full term(39–41 weeks)N = 1,545,051	Post term(≥42 weeks)N = 205,588
Child characteristics, n (%)
Sex
Male	967 (50.9)	11,557 (55.3)	45,965 (54.9)	197,810 (52.3)	784,172 (50.7)	107,329 (52.2)
Female	933 (49.1)	9,323 (44.7)	37,727 (45.1)	180,091 (47.7)	760,879 (49.3)	98,259 (47.8)
Birth order
1	936 (49.3)	10,480 (50.2)	40,531 (48.4)	150,239 (39.8)	631,707 (40.9)	98,770 (48.0)
2	528 (27.8)	5,910 (28.3)	25,028 (29.9)	137,280 (36.3)	582,561 (37.7)	68,514 (33.3)
≥3	436 (22.9)	4,490 (21.5)	18,133 (21.7)	90,382 (23.9)	330,783 (21.4)	38,304 (18.6)
Fetal growth
SGA	12 (0.6)	2,225 (10.7)	7,613 (9.1)	26,114 (6.9)	144,519 (9.3)	43,117 (21.0)
AGA	1,722 (90.6)	17,500 (83.8)	68,377 (81.7)	307,966 (81.5)	1,243,289 (80.5)	149,174 (72.6)
LGA	166 (8.7)	1,155 (5.5)	7,702 (9.2)	43,821 (11.6)	157,243 (10.2)	13,297 (6.5)
Maternal characteristics, n (%)
Age (years)
<20	102 (5.4)	1,322 (6.3)	4,625 (5.5)	15,066 (4.0)	59,301 (3.8)	10,454 (5.1)
20–29	994 (52.3)	11,936 (57.2)	50,044 (59.8)	224,019 (59.3)	977,540 (63.3)	135,023 (65.7)
30–39	753 (39.6)	7,054 (33.8)	27,168 (32.5)	130,463 (34.5)	488,095 (31.6)	58,020 (28.2)
≥40	51 (2.7)	568 (2.7)	1,855 (2.2)	8,353 (2.2)	20,115 (1.3)	2,091 (1.0)
Education (years)
≤9	360 (19.0)	3,984 (19.1)	15,048 (18.0)	61,382 (16.2)	226,364 (14.7)	33,205 (16.1)
10–12	981 (51.6)	10,865 (52.0)	42,949 (51.3)	189,491 (50.1)	769,943 (49.8)	101,932 (49.6)
>12	559 (29.4)	6,031 (28.9)	25,695 (30.7)	127,028 (33.6)	548,744 (35.5)	70,451 (34.3)
Birth country or region
Sweden	1,559 (82.1)	18,145 (86.9)	73,421 (87.7)	330,078 (87.4)	1,378,845 (89.2)	185,172 (90.1)
Other Europe/US/Canada	228 (12.0)	1,833 (8.8)	6,858 (8.2)	30,178 (8.0)	113,368 (7.3)	14,761 (7.2)
Asia/Oceania	69 (3.6)	594 (2.8)	2,329 (2.8)	12,313 (3.3)	35,532 (2.3)	3,438 (1.7)
Africa	11 (0.6)	125 (0.6)	432 (0.5)	1,975 (0.5)	7,161 (0.5)	1,138 (0.5)
Latin America	19 (1.0)	108 (0.5)	467 (0.6)	2,690 (0.7)	7,842 (0.5)	755 (0.4)
Other/unknown	14 (0.7)	75 (0.4)	185 (0.2)	667 (0.2)	2,303 (0.2)	324 (0.2)
BMI (kg/m2)
<18.5	34 (1.8)	504 (2.4)	2,693 (3.2)	11,961 (3.2)	36,196 (2.3)	2,726 (1.3)
18.5–24.9	1,727 (90.9)	18,925 (90.6)	74,603 (89.1)	334,819 (88.6)	1,390,055 (90.0)	188,541 (91.7)
25.0–29.9	98 (5.2)	1,082 (5.2)	4,905 (5.9)	24,515 (6.5)	95,226 (6.2)	11,181 (5.4)
≥30.0	41 (2.2)	369 (1.8)	1,491 (1.8)	6,606 (1.7)	23,574 (1.5)	3,140 (1.5)
History of lipid disorder	400 (21.1)	4,825 (23.1)	18,232 (21.8)	75,406 (20.0)	275,535 (17.8)	40,065 (19.5)

Abbreviations: AGA, appropriate for gestational age; BMI, body mass index; LGA, large for gestational age; SGA, small for gestational age

Associations between gestational age at birth and lipid disorders

A total of 25,050 (1.1%) persons were identified with lipid disorders in 30.3 million person-years of follow-up. The incidence rate (per 100,000 person-years) was 82.79 in the overall cohort, 101.89 among those born preterm, and 80.12 among those born full term (Table 2).

Table 2

Adjusted HRs for lipid disorders associated with gestational age at birth, Sweden, 1973–2016.

Gestational age at birth	Cases	Ratea	Unadjusted		Adjusted for child characteristicsb		Adjusted for child and maternal characteristicsc
			HR (95% CI)	P	HR (95% CI)	P	HR (95% CI)	P
Preterm (<37 wks)	1,426	101.89	1.32 (1.25–1.40)	<0.001	1.29 (1.22–1.36)	<0.001	1.23 (1.16–1.29)	<0.001
Extremely preterm (22–27 wks)	31	148.78	2.24 (1.57–3.18)	<0.001	2.11 (1.49–3.01)	<0.001	2.00 (1.41–2.85)	<0.001
Very preterm (28–33 wks)	301	111.64	1.47 (1.31–1.64)	<0.001	1.42 (1.26–1.59)	<0.001	1.33 (1.19–1.49)	<0.001
Late preterm (34–36 wks)	1,094	98.63	1.27 (1.20–1.36)	<0.001	1.24 (1.17–1.32)	<0.001	1.19 (1.12–1.26)	<0.001
Early term (37–38 wks)	4,187	85.99	1.15 (1.11–1.19)	<0.001	1.11 (1.08–1.15)	<0.001	1.09 (1.05–1.13)	<0.001
Full term (39–41 wks)	16,712	80.12	Reference		Reference		Reference
Post term (≥42 wks)	2,725	87.04	0.96 (0.93–1.00)	0.08	1.00 (0.96–1.04)	0.93	0.99 (0.95–1.03)	0.62
Per additional 5 weeks (trend)			0.80 (0.77–0.82)	<0.001	0.83 (0.81–0.86)	<0.001	0.86 (0.83–0.89)	<0.001

aIncidence rate per 100,000 person-years.

bAdjusted for birth year, sex, and birth order.

cAdjusted for birth year, sex, birth order, and maternal characteristics (age, education, birth country or region, body mass index, history of lipid disorder).

Abbreviations: HR, hazard ratio

Gestational age at birth was inversely associated with risk of lipid disorders. Each additional 5 weeks of gestation were associated with a 14% lower risk on average (full model: HR, 0.86; 95% CI, 0.83–0.89; P < 0.001; Table 2). Preterm and extremely preterm birth were associated with 1.2- and 2.0-fold risks, respectively (full model: HR, 1.23; 95% CI, 1.16–1.29; P < 0.001; and 2.00; 95% CI, 1.41–2.85; P < 0.001). Early-term birth also was associated with a slightly increased risk of lipid disorders, relative to full-term birth (full model: HR, 1.09; 95% CI, 1.05–1.13; P < 0.001). Fig 1 shows a forest plot of adjusted HRs and 95% CIs for different gestational age groups compared to full-term birth.

Fig 1

Adjusted HRs and 95% CIs for risk of lipid disorders by gestational age at birth compared to full-term birth, Sweden, 1973–2016.

HR, hazard ratio.

Most of the adjusted HRs were <10% lower than the corresponding unadjusted HRs (Table A in S1 Appendix); neither birth year nor any variables in Table 1 were major confounders. Additional adjustment for fetal growth also had a negligible effect on the risk estimates (e.g., full model, preterm versus full term: HR, 1.23; 95% CI, 1.17–1.30; P < 0.001). The proportional hazards assumption was assessed by examining log-log plots [32] and was met in each model.

Co-sibling analyses

In co-sibling analyses to control for unmeasured shared familial factors, all risk estimates were substantially attenuated compared with the primary results. For example, in the full model, the adjusted HRs for lipid disorders associated with preterm and extremely preterm birth, respectively, were 1.10 and 1.46 in the co-sibling analysis (Table 3) compared with 1.23 and 2.00 in the primary analysis (Table 2).

Table 3

Co-sibling analyses for gestational age at birth in relation to risk of lipid disorders, Sweden, 1973–2016.

Gestational age at birth	Cases	Ratea'	Adjusted for shared familial factorsb		Additionally adjusted for child and maternal characteristicsc
			HR (95% CI)	P	HR (95% CI)	P
Preterm (<37 wks)	1,006	93.86	1.14 (1.01–1.28)	0.03	1.10 (0.97–1.24)	0.13
Extremely preterm (22–27 wks)	19	123.62	1.61 (0.75–3.45)	0.22	1.46 (0.68–3.15)	0.33
Very preterm (28–33 wks)	203	101.91	1.15 (0.90–1.48)	0.25	1.09 (0.85–1.40)	0.49
Late preterm (34–36 wks)	784	91.45	1.12 (0.99–1.28)	0.08	1.09 (0.96–1.24)	0.19
Early term (37–38 wks)	3,077	78.97	1.10 (1.03–1.18)	0.005	1.06 (0.98–1.13)	0.13
Full term (39–41 wks)	12,413	74.31	Reference		Reference
Post term (≥42 wks)	2,004	82.57	0.97 (0.89–1.05)	0.41	1.02 (0.93–1.11)	0.69
Per additional 5 weeks (trend)			0.86 (0.80–0.93)	<0.001	0.93 (0.86–1.00)	0.05

aIncidence rate per 100,000 person-years.

bAdjusted for shared familial (genetic and/or environmental) factors.

cAdditionally adjusted for specific child characteristics (birth year, sex, birth order) and maternal characteristics (age, education, birth country or region, body mass index, history of lipid disorder).

Abbreviations: HR, hazard ratio

In sensitivity analyses, the appropriateness of the co-sibling design was assessed by exploring generalizability of the primary findings to persons without siblings. The risk estimates were only slightly lower in persons with siblings (e.g., preterm versus full term: adjusted HR, 1.20; 95% CI, 1.13–1.28) compared with those without siblings (1.25; 95% CI, 1.13–1.38; P for interaction = 0.41). In a meta-analysis that combined this latter risk estimate (i.e., from persons without siblings) with that from the co-sibling analysis (restricted to persons with siblings), the pooled HR for lipid disorders associated with preterm versus full-term birth was 1.12 (95% CI, 1.01–1.25; P = 0.03).

We also assessed for potential bias from carryover effects of preterm birth in one sibling affecting the outcome in another sibling [25-27] by comparing results from families in which the first child was born preterm to those in which the second child was born preterm. These analyses yielded similar results (adjusted HRs 1.2–1.3), suggesting that there was no bias from an exposure-to-outcome carryover effect.

Interactions

Lipid disorders had a higher incidence among men than women in the overall cohort (97.60 versus 67.01 per 100,000 person-years) and among those born preterm (117.33 versus 82.69). However, the adjusted HR for lipid disorders associated with preterm birth was similar among men (1.22; 95% CI, 1.14–1.31; P < 0.001) and women (1.23; 1.12–1.34; P < 0.001; Table B in S1 Appendix). No significant interactions were found between preterm birth and sex on the additive (P = 0.43) or multiplicative (P = 0.66) scale (Table C in S1 Appendix). The absence of additive interaction suggests that preterm birth accounted for a similar number of lipid disorder cases among men and women.

Potential interactions between gestational age at birth and fetal growth were also explored. The highest risk of lipid disorders occurred among those born SGA at early term (adjusted HR, 1.59; 95% CI, 1.45–1.75; P < 0.001) or SGA and preterm (1.50; 1.28–1.76; P < 0.001), relative to AGA and full-term. A positive interaction was found between SGA and early-term birth on both the additive (P = 0.003) and multiplicative (P = 0.01) scale (i.e., their combined effects on risk of lipid disorders exceeded the sum or product of their separate effects). However, there was no evidence of interaction between fetal growth and preterm birth (additive, P = 0.97; multiplicative, P = 0.60; Table D in S1 Appendix).

The association between preterm birth and risk of lipid disorders was slightly stronger among persons delivered by Caesarean section (adjusted HR, 1.32; 95% CI, 1.18–1.48; P < 0.001; N = 225,315 [10.1% of the cohort]) than those delivered vaginally (1.18; 95% CI, 1.11–1.26; P < 0.001; N = 2,009,469 [89.9%]; tests for interaction: additive, P = 0.06; multiplicative, P = 0.09).

Other sensitivity analyses

Among 24,403 persons diagnosed with lipid disorders at least one year after inception of the pharmacy register, 354 (25.6%) of those born preterm and 4,098 (25.1%) of those born full-term had ≥2 prior prescriptions of an atherogenic medication (as defined above). Exclusion of these cases from the study outcome resulted in a negligible change in risk estimates. For example, in the most fully adjusted model, the HR for lipid disorders associated with preterm birth was 1.22 (95% CI, 1.15–1.29; P < 0.001) when including these cases, compared with 1.23 (95% CI, 1.16–1.32; P < 0.001) after excluding them. The corresponding HRs associated with extremely preterm birth were 1.91 (95% CI, 1.31–2.79; P = 0.001) and 1.99 (95% CI, 1.28–3.08; P = 0.002), respectively. These findings suggest that lipid disorders that were secondary to medications had little influence on the overall results.

In a complete case analysis performed as an alternative to multiple imputation, all risk estimates were similar to those from the primary analysis (e.g., full model, preterm versus full term: HR, 1.23; 95% CI, 1.08–1.39; P = 0.002). When the primary analyses were repeated after restricting to persons born in 1982 or later (when gestational ages were estimated predominantly by ultrasound), the risk estimates were also minimally changed (e.g., full model, preterm versus full term: HR, 1.25; 95% CI, 1.14–1.37; P < 0.001).

Discussion

In this large national cohort study, preterm birth was associated with a modestly (20%–25%) increased risk of lipid disorders among men and women in early- to midadulthood. Stronger associations were seen at earlier gestational ages, including a 2-fold risk among those born extremely preterm (<28 weeks). Co-sibling analyses suggested that these findings were partially related to shared familial factors that are associated with both preterm birth and lipid disorders, as opposed to direct effects of preterm birth.

To our knowledge, this is the first large population-based study to examine gestational age at birth in relation to risk of lipid disorders in adulthood. Previous evidence from smaller studies has been conflicting. The largest prior study assessed plasma lipid levels in a British cohort of 7,847 adults aged 44 to 45 years and reported a weak inverse association between gestational age at birth and total cholesterol only in women (difference in mean level per additional week of gestation: −0.02; 95% CI, −0.05 to −0.001; P = 0.04; adjusted for birth weight, BMI, and other perinatal and sociodemographic factors) [12]. However, gestational age was not associated with total cholesterol in men, nor with LDL-C, high-density lipoprotein cholesterol (HDL-C), or triglyceride levels in either women or men. Other small clinical studies with up to a few hundred participants also have explored gestational age at birth in relation to lipid levels in early adulthood. Some [13-15] but not all [16-19] have reported higher levels of total cholesterol, LDL-C, or triglycerides in young adults (mean ages 25–40 years) who were born preterm or with low birth weight compared with their full-term or normal birth weight counterparts. A meta-analysis of such studies found that preterm birth was associated with significantly higher LDL-C levels compared with full-term birth (0.15 mmol/L; 95% CI, 0.01–0.30; P = 0.04; based on 5 studies) and near-significantly higher total cholesterol levels (0.32 mmol/L; −0.01 to 0.65; P = 0.05; 6 studies) but no differences in HDL-C or triglyceride levels (8 and 9 studies, respectively) [33].

The present study extends these prior findings by assessing lipid disorders in a large population-based cohort using nationwide diagnoses and medication prescriptions and examining sex-specific differences and potential confounding by shared familial factors. In this cohort and in other general populations, lipid disorders have a higher overall prevalence among men [12]. However, our findings suggest that preterm birth accounted for a similar number of lipid disorder cases among men and women. Furthermore, the observed associations between preterm birth and lipid disorders appeared to be substantially though not completely explained by shared genetic or environmental factors in the families of those affected. This is in contrast to previously reported associations between preterm birth and IHD, mortality, and other outcomes in this cohort that appeared largely independent of shared familial factors [5, 22, 34, 35]. Sensitivity analyses suggested that the attenuated risk estimates in co-sibling analyses were unlikely to be explained by idiosyncratic differences in persons with siblings compared to those without.

Family- and twin-based studies have suggested that genetic factors inherited primarily from the mother influence gestational age at birth, with an estimated heritability of 25% to 40% [36-38]. In addition, heritability estimates for different lipid components range from 28% to 92% [39, 40], with numerous genetic loci identified [41]. Several functional mutations, including in the proprotein convertase subtilisin kexin 9 gene (PCSK9) and the apolipoprotein B gene (e.g., APOB3500), have been found to impair LDL receptor-mediated catabolism of LDL-C [41-43]. LDL-C is also a known precursor of progesterone synthesis during pregnancy [44]. Prior studies have suggested that either high or low total cholesterol levels preceding or during pregnancy are associated with increased risk of preterm delivery [45, 46]. Maternal total cholesterol levels during the first trimester and their change during pregnancy have been found to predict preterm delivery [47]. Additional clinical and genetic studies are needed to further elucidate the mechanisms and possible shared genetic factors that might link preterm birth with lipid disorders.

Findings from the present study may have several clinical implications. First, they show that preterm birth, especially at the earliest gestational ages, may be an important risk factor for lipid disorders in adulthood. A higher incidence of lipid disorders may contribute to the higher risks of metabolic syndrome and cardiovascular disease (CVD) previously reported in adults who were born prematurely [5, 22, 33, 48]. Early preventive evaluation and monitoring for lipid disorders should be incorporated in the long-term care of persons who were born prematurely. To help identify such patients, medical records and history taking at all ages should routinely include birth history, including gestational age, birth weight, and perinatal complications [49-51]. Such information can help trigger anticipatory screening, timely treatment, and early preventive actions, including lifestyle counseling to help reduce the risk of lipid disorders and subsequent risk of CVD.

A key strength of the present study was the ability to examine gestational age at birth in relation to lipid disorders for the first time in a large population-based cohort with follow-up into early- to midadulthood, using nationwide birth, medical, and pharmacy registry data. This study design minimizes potential selection or ascertainment biases and enables more robust risk estimates based on a national population. The large sample size enabled well-powered assessments of narrowly defined gestational age groups and sex-specific analyses. The results were controlled for other perinatal and maternal factors as well as unmeasured shared familial factors using co-sibling analyses.

Limitations include the unavailability of laboratory data to verify diagnoses. However, high positive predictive values for most chronic disorders have been reported in the Swedish registries [23], and the Swedish national health system may reduce disparities in healthcare access and help improve ascertainment of lipid disorders in the general population. We were unable to assess the severity of lipid disorders or distinguish more specific disorders affecting lipid components (e.g., LDL-C, HDL-C, or triglycerides). We lacked information on spontaneous versus medically indicated preterm birth, which was not systematically recorded during most years of this birth cohort. In addition, we lacked information on diet, physical activity, or smoking, which may potentially modify the association between preterm birth and lipid disorders. Assessment of behavioral factors later in life would be useful in future studies with access to this information. Lipid disorders were assessed at ages 18 to 44 years in the present study, and thus additional follow-up will be needed to examine this outcome in later adulthood. Lastly, this study was limited to Sweden and will need replication in other countries when possible, including diverse populations that would allow assessment for potential racial or ethnic heterogeneity.

In summary, we found that preterm birth was associated with an increased risk of lipid disorders in early- to midadulthood in a large population-based cohort. These findings did not appear to be explained by atherogenic medications or lipid disorders that developed in childhood or adolescence. However, co-sibling analyses suggested that they were partially due to shared familial (genetic and/or environmental) factors that predispose to both preterm birth and lipid disorders. Future studies of shared genetic factors that influence both the timing of delivery and lipid metabolism may help further elucidate the potential mechanisms. The associations we found between preterm birth and lipid disorders may partially mediate the increased CVD risks previously reported in adults born prematurely [5]. Persons with a history of preterm birth may need early preventive evaluation and long-term monitoring for lipid disorders to reduce their future risks of CVD.

STROBE checklist.

STROBE, strengthening the reporting of observational studies in epidemiology

(DOCX)

Click here for additional data file.

(Table A) Unadjusted HRs for lipid disorders associated with gestational age at birth, stratified by sex, Sweden, 1973 to 2016. (Table B) Adjusted HRs for lipid disorders associated with gestational age at birth, stratified by sex, Sweden, 1973 to 2016. (Table C) Interactions between gestational age at birth and sex in relation to risk of lipid disorders at ages 18 to 44 years. (Table D) Interactions between gestational age at birth and fetal growth in relation to risk of lipid disorders at ages 18 to 44 years. HR, hazard ratio.

(DOCX)

Click here for additional data file.

29 Jul 2019

Dear Dr. Crump,

Thank you very much for submitting your manuscript "Preterm Birth and Risk of Lipid Disorders in Adulthood: A National Cohort Study" (PMEDICINE-D-19-01959) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

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Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Clare Stone

Acting Chief Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Abstract – please state which country this is based in and also in title.

Abstract – Please add demographic details and include a sentence on the limitations of the study as the final sentence of the ‘methods and Findings section’. Please include p values for all 95%CIs (and in the main text). ‘these findings were similar in men and women’ – please be accurate; similar is ambiguous.

Data – you mention that some restrictions will apply, then you say all data is available. Can you please clarify, with the caveat that PLOS has a data availability policy. PLOS Medicine requires that the de-identified data underlying the specific results in a published article be made available, without restrictions on access, in a public repository or as Supporting Information at the time of article publication, provided it is legal and ethical to do so. Please see the policy at

http://journals.plos.org/plosmedicine/s/data-availability

and FAQs at

http://journals.plos.org/plosmedicine/s/data-availability#loc-faqs-for-data-policy

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

"was associated" at line 303; "need" seems too strong at lines 41 and 309

remove page numbers from STROBE checklist – this should be sections and paragraphs, as they can change during page layout.

Causal language – Line 274 – “have several clinical implications” as this is not a trial and an observational study, please tone down causal, overheated language – for example, ‘may have’; also Line 275 “should” replace with ‘may’ and again “should be recognized as a risk factor”…’may be taken into account when considering’ ….Please adjust throughout where such language occurs.

Comments from the reviewers:

Reviewer #1: See attachment

Michael Dewey

Reviewer #2:

The authors examined longitudinal associations of preterm birth with a risk of developing lipid disorders in young adulthood (age 18-44). This study is unique and highlighting the potential of shaping a national database into a birth cohort for research on adult-onset diseases.

However, the strength of clinical implications was challenging to judge, and there are several concerns. The reviewer is providing main and minor comments hereafter.

Major comments:

1. The authors defined "lipid disorders" using ICD codes. The codes included ICD-8/9: 272 and ICD-10: E66.

The latter includes the followings:

E66 Overweight and obesity

- E66.0 Obesity due to excess calories

- E66.1 Drug-induced obesity

- E66.2 Morbid (severe) obesity with alveolar hypoventilation

- E66.3 Overweight

- E66.8 Other obesity

- E66.9 Obesity, unspecified

ICD-10 E66 is for obesity or overweight, not for lipid disorders. It may be uncommon for adults <44 years go to a clinic just because they are overweight or obese. So, the observed low risk of being recorded with "E66" would make sense. The authors should check back what information the authors extracted. If the authors had used E66 literally, the authors should revise it to study lipid disorders as the authors aimed and redo all the analysis.

1. The validity of the longitudinal analysis is unclear or not identifiable. The authors evaluated lipid disorders at ages 18-44 years as "lipid disorders in adulthood". Therefore, the authors should not count any lipid disorders before participants became 18 years old. Also, there is a possibility that a lipid disorder occurred during a fetal period and caused a preterm delivery. It is not clear how the authors treated those individuals who developed such a fetal, infantile, or juvenile lipid disorder.

The authors are describing that one of the inclusion criteria for this cohort was "still living in Sweden at age 18 years". This description did not rule out the possibility that entry of adolescents with a lipid disorder into the cohort.

The authors should confirm and clarify that the authors had evaluated participants who were free from a lipid disorder at the age of 18 years. If the authors were not sure, the authors should do additional analyses, for example, excluding those who were treated at least once with lipid-lowering drugs during 18-20 years of age.

Sipola-Leppanen et al. reported the association of preterm delivery with unfavourable lipid profiles in adolescents (Pediatrics, 2014;134(4):e1072-81). Other studies exist sufficiently to raise the concern about censoring and the risk set in this study.

2. The clinical relevance may be much weaker than the extent which the authors are describing. This point may be crucial and should not be discussed just as a limitation or weakness in the Discussion. Analysis should deal with it.

We should note that lipid disorders in this manuscript were those developed before the age of 44 years as the primary outcome. The average incidence rate was approximately 0.1% (100/100000) (Table 2). The clinical implications for such a rare event may require the focus on many possible rare conditions in childhood.

The concern is about secondary dyslipidemia. Many children or young adults may have experienced an onset of respiratory disease, dermatological disease, an endocrine disorder, or another. They might receive medications which influence lipid homeostasis and induce secondary dyslipidemia (e.g. Henkin et al., Secondary Dyslipidemia Inadvertent Effects of Drugs in Clinical Practice, JAMA, 1992;267(7):961-968).

For participants treated with other medications for their primary disease, lipid profiles are subject to monitoring and treatment. For clinicians who often face secondary dyslipidemia anyhow, this paper may not be informative, and the authors may not need to highlight the importance.

This point partly relates to the comment above about a lipid disorder or any other diseases arising before aged 18 years. The authors should account for this plausibility over the course from the analysis stage (see the other comments) to the interpretation and discussion.

The authors discuss clinical implications on Page 13. This part may represent what paediatricians already know. Birth outcomes, including preterm delivery, may influence a child-onset disease that requires a particular medication and the medical condition further influences lipid profiles and further cardiovascular health in later life. If that is a causal pathway, the authors' implication would require revision.

In addition to secondary dyslipidemia, preterm birth may relate to a growth spurt and later obesity or overweight. Concerning the obesity issue, physicians commonly pay attention to preterm birth and childhood obesity. Then, lipid disorders and other cardiometabolic outcomes may get concerning reasonably. Regarding many correlated issues and causal pathways, clinicians may have already recognised the importance of preterm delivery for cardiovascular risk factors, not only lipid disorders. From this broad view, again, the clinical implication of this study seems to be weak. The authors need to account for related, well-known issues which pediatricians have already know.

3. The authors state the availability of primary and secondary diagnoses (Line 94-108). The authors seem to be able to identify different types of medications, not only lipid-lowering medications.

Therefore, the authors should address the concern of secondary dyslipidemia above raised. Note that some observational studies using an extensive medical database have incorporated as much information as possible, including different types of medications and clinical diagnoses (e.g. Hippsley-Cox et al., BMJ, 2017;357:j2099). The similar effort would be crucial.

4.

Co-sibling analyses are unique and informative for readers. Future readers will be careful of the potential of using a national database containing similar types of information.

The authors described that the sample size of the co-sibling analysis was approximately 80% of the total number of participants. The authors should explain or revise those participants fully contributed to the co-sibling analysis. For example, if one family has two siblings, and if both did not develop lipid disorders, the within-family matched analysis would not use them. With the rarity of the events, effective sample sizes of the co-sibling analyses might be much smaller than those presented. The authors should verify and clarify it. (note: the continuous estimate in Table 3 had nearly five times greater variance than that in Table 2.)

The authors' interpretation from the co-sibling analyses seems to be valid, but at the same time, the authors may want to describe the nature of the uncertainty as well.

Minor comments:

Table 2 and Table 3 do not need to show the results after sex-stratification. The results by sex were unremarkable. There is no clear, strong rationale or hypothesis to indicate the effect modification by sex. Also, equivalent and additional pieces of information are available in the supplementary materials.

In both Table 2 and Table 3, the authors should include hazard ratios (95% confidence intervals) from different regression models. Unadjusted HRs are worth presenting. Changes in HRs via different adjustments could be meaningful to present (e.g. adjustment for child characteristics with or without adjustment for maternal characteristics). "Preterm or not" showed unadjusted HR=1.32 and adjusted HR=1.23. The shift is informative about mechanisms of the complex association of early-life environment with lipid disorders recorded in later life.

For the analysis treating gestational age as a continuous variable, the authors may better use hazard ratio per 5 weeks. One week is of a too narrow range given the distribution of the gestational ages, and the point estimates were not informative. The point estimates and 95% CIs in Table 2 and Table 3 were informative enough for some readers to calculate p-values. The authors may better avoid inconsistent presentation between Table 2 and Table 3 (i.e. p-values, which the authors may delete from Table 2).

Methods:

The reviewer wonders if the information on C-section was available.

Results:

Some text, Page 10 in particular, was hard to read. Readers expect to read results, not descriptions of the methods, rationales, and interpretations. Those should be in Methods or Discussion. The authors should document what results were from which analysis.

The results of the co-sibling analyses should include information on the number of families, not only individual adults.

Line 201-204: The text seems that the authors are obsessed with statistical significance. "slightly lower" is just fine with p for interaction = 0.41. Not wrong. The next sentence should be in the Discussion section.

Line 206-213: The authors should note the analysis of within-family carryover effects in Methods. Then, the sample size should be clarified. This analysis must have included families with discordant outcomes in their siblings only. The authors should not use the phrase "case-crossover" as it is confusing and should correspond to a particular study design which the authors did not adopt.

In the Discussion, the authors cited information on genetic factors influencing both timing of delivery and lipid disorder in an offspring. The authors bind this to the finding just once in the Discussion.

Figures/Tables:

Figure 1 should not include the label of 0.00 as 0.00 is not possible. Also, the authors should provide each point estimate and 95% CI on the right of the panel. The horizontal axis covers a too wide range of HRs, and then uncertainty (CIs) give a false impression of narrow ones. Present the range up to 2.5 rather than 3.0 and use an arrow to the right.

The reviewer reserves other minor comments at this moment.

Reviewer #3: This work reveal important associations between how gestational lengths may effect serious complications later in life, lipid disorders, which is the underlying cause of cardiovascular diseases in adults. This knowledge is important for understanding how diseases arise and but could also be important for clinicians to recognize early risk factors in patients and encourage life style changes in reducing the risk of diseases to improve public health.

The paper is well written. The research presented in the paper is of good quality and is scientifically sound. I have only some minor comments:

1. In the abstract line 2, it would be interesting to know what type of disorders you are referring too, if word counts accept this.

2. Line 26, comment on type of cohort study, a retrospective design?

3. Line 27, why did you choose this time period? Many cases are still young. Would it be easier to see associations in older age groups? Later in the manuscript I did understand that you choose this period because of the registry and no data was accessible before this time period. If you had access to data from older age groups, how do you think this would have affected the results?

4. Line 34, why did you use full-term birth only gestational week 39-41?

5. Line 50, again a short introduction about what type of lipid disorders you are referring to.

6. Line 86, gestational length is collected from both last menstruation period and ultrasound. How do you this this affects the results in the study?

7. Line 114. Some covariates are missing e.g. smoking, physical activity and diet. How do you think this would impact the results if they were available?

8. In the discussion part you are presenting some studies that has looked at specific lipids as for example triglycerides. Triglycerides are more of a risk factor for CVD in women. Was it not possible to analyze from the Registry?

9. Why did you choose Cox regression model?

Overall, the paper is presenting convincing research results and I believe it is a good candidate for being published in PLOS Medicine.

Any attachments provided with reviews can be seen via the following link:

[LINK]

Submitted filename: crump.pdf

Click here for additional data file.

12 Aug 2019

Submitted filename: Crump_Response_to_Reviewers_PLOS_Med.docx

Click here for additional data file.

29 Aug 2019

Dear Dr. Crump,

Thank you very much for submitting your manuscript "Preterm Birth and Risk of Lipid Disorders in Early Adulthood: A Swedish Cohort Study" (PMEDICINE-D-19-01959R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Sep 05 2019 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Gordon Smith,

Obstetrics & Gynaecology

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Please address all points from Rev 2. Thank you.

Comments from the reviewers:

Reviewer #2:

The reviewer has thought that the authors revised the manuscript thoroughly. Many of the previous concerns have now disappeared. The reviewer provides additional comments on the current manuscript hereafter.

Main comments:

The authors did the primary analysis pooling all siblings within the same families and those without siblings. This analysis (at least conceptually) involved bias because the authors assumed that all the observations were independent while those within the same families were not independent.

The reviewer suggests the authors do the followings:

1) conduct the co-sibling analysis (already done).

2) do the standard analysis of participants without any siblings (i.e. those whom the authors excluded in the co-sibling analysis), and

3) meta-analyze the two estimates.

Each output from these three analyses (the latter two, undone/unavailable yet) is worth presenting. The authors may do after stating that it is post hoc.

(additionally, the authors may want to do an additional analysis after selecting participants without siblings and also selecting only one participant from each family with multiple participants. This analysis would be proper without violation of the assumption that all individuals were independent.)

Minor comments:

In the description of the co-siblings data, the reviewer suggests that the authors clarify that they examined the association of gestational ages at birth with time to events within the family. It would be difficult to understand what the authors stated, "comparisons of different gestational ages at birth are made within the family." (also this should be in the past tense.)

The reviewer suggests the authors not to state "fully adjusted" (Line 213-222, for example). Observational studies cannot make "full" adjustment without perfect randomization. The authors should rephrase it to mean that they did the best effort over the availability of their data, saying "most adjusted" or something similar.

Line 241-245: the authors should move this part to Methods. If the authors want to re-emphasize what the analysis would mean, the authors may state it in a phrase. Then, the sentence could be: "In co-sibling analyses controlling for unmeasured shared genetic or environmental factors within families, all risk estimates were substantially attenuated (Table 3) in comparison to the primary results."

Line 249-251: The authors should move this interpretation to Discussion.

Likewise, redundant information across Methods, Results, and Discussion is subject to minor revisions to make the manuscript succinct.

Line 372-382: The authors may better highlight that the findings were likely to be independent of secondary dyslipidemia and so on. The reviewer is now supportive of the clinical implication of this study. On the other hand, the reviewer suggests the authors acknowledge dyslipidemia in the pediatrics field or drug-induced dyspilidemia. This approach will be better, considering readers who have already been working on dyslipidemia in youths and young adults.

Any attachments provided with reviews can be seen via the following link:

[LINK]

6 Sep 2019

Dear Dr. Crump,

Thank you very much for re-submitting your manuscript "Preterm Birth and Risk of Lipid Disorders in Early Adulthood: A Swedish Cohort Study" (PMEDICINE-D-19-01959R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

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If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Sep 13 2019 11:59PM.

Sincerely,

Clare Stone Acting Editor-in-Chief

PLOS Medicine

plosmedicine.org

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Requests from Editors:

Title – association with, instead of risk of

Abstract - Please add a sentence on the limitations of your study as the final sentence of the Methods and Findings section of the abstract.

- around line 42, add "The main study limitations were ..." or similar

- around line 100 or line 150, state whether there was an analysis plan, and if so attach the document as a Supp file. If no analysis plan exists please state when the analyses were planned in relation to the data analysis.

- update reference 5, or please provide a copy of the acceptance letter

- ref 34 has information that can be trimmed – please refer to style guide for refs.

Comments from Reviewers:

Any attachments provided with reviews can be seen via the following link:

[LINK]

11 Sep 2019

Submitted filename: Crump_Response_to_Reviewers_PLOS_Med_R3_v2.docx

Click here for additional data file.

12 Sep 2019

Dear Dr. Crump,

On behalf of my colleagues and the academic editor, Dr. XXX, I am delighted to inform you that your manuscript entitled "Association of Preterm Birth with Lipid Disorders in Early Adulthood:  A Swedish Cohort Study" (PMEDICINE-D-19-01959R3) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point.

PRESS

A selection of our articles each week are press released by the journal. You will be contacted nearer the time if we are press releasing your article in order to approve the content and check the contact information for journalists is correct. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact.

PROFILE INFORMATION

Now that your manuscript has been accepted, please log into EM and update your profile. Go to https://www.editorialmanager.com/pmedicine, log in, and click on the "Update My Information" link at the top of the page. Please update your user information to ensure an efficient production and billing process.

Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Gordon Smith,

Obstetrics & Gynaecology

PLOS Medicine

plosmedicine.org