Daily Endocrinology Research Analysis
Today's most impactful endocrinology research spans genetics, perinatal metabolism, and measurement science. A nationwide family-based genetic study links parental autoimmune diseases to offspring type 1 diabetes risk via both HLA and non-HLA variants and proposes a parental polygenic score. A large prospective cohort shows maternal anemia reshapes neonatal metabolomic profiles, especially fatty acid beta-oxidation, while a doubly labeled water–anchored methods paper advances how we identify pla
Summary
Today's most impactful endocrinology research spans genetics, perinatal metabolism, and measurement science. A nationwide family-based genetic study links parental autoimmune diseases to offspring type 1 diabetes risk via both HLA and non-HLA variants and proposes a parental polygenic score. A large prospective cohort shows maternal anemia reshapes neonatal metabolomic profiles, especially fatty acid beta-oxidation, while a doubly labeled water–anchored methods paper advances how we identify plausible dietary energy intake in obesity research.
Research Themes
- Intergenerational genetic risk architecture in type 1 diabetes
- Maternal anemia and neonatal metabolic programming
- Validity of dietary energy intake assessment using doubly labeled water
Selected Articles
1. Effects of parental autoimmune diseases on type 1 diabetes in offspring can be partially explained by HLA and non-HLA polymorphisms.
Using nationwide Finnish registers and FinnGen genetics, the study shows that 15 parental autoimmune diseases are associated with higher type 1 diabetes risk in offspring and that much of this aggregation is explained by both HLA and non-HLA variants. A parental polygenic score integrating these loci is proposed to characterize offspring risk.
Impact: This work integrates within-family genetic transmission with population-scale data to clarify intergenerational T1D risk architecture and introduces a pragmatic PGS concept for parental counseling.
Clinical Implications: Parental autoimmune history combined with an HLA/non-HLA–based parental PGS could refine offspring T1D risk stratification, informing early monitoring and prevention strategies.
Key Findings
- Among 50 parental autoimmune diseases, 15 were associated with increased offspring T1D risk using 58,284 trios from FinRegistry.
- Within-family polygenic transmission analysis in 12,563 trios showed that aggregation of parental AIDs with offspring T1D is partially explained by HLA and non-HLA polymorphisms.
- A parental polygenic score incorporating HLA and non-HLA variants was proposed to characterize cumulative offspring T1D risk; analyses leveraged 470,000 genotyped Finns.
Methodological Strengths
- Nationwide registry-based trios combined with 470,000-genotype data for high statistical power
- Within-family polygenic transmission analysis reduces confounding by population stratification
- Partitioning HLA vs non-HLA effects clarifies disease-specific genetic architecture
Limitations
- Observational genetic design cannot establish causal environmental pathways
- Generalizability may be limited to Finnish/European ancestries
- Clinical utility of the proposed parental PGS requires prospective validation and ethical evaluation
Future Directions: Prospectively validate parental PGS across diverse populations, integrate with non-genetic risk factors, and assess clinical thresholds and ethical frameworks for counseling.
Type 1 diabetes (T1D) and other autoimmune diseases (AIDs) often co-occur in families. Leveraging data from 58,284 family trios in Finnish nationwide registers (FinRegistry), we identified that, of 50 parental AIDs examined, 15 were associated with an increased T1D risk in offspring. These identified epidemiological associations were further assessed in 470,000 genotyped Finns from the FinnGen study through comprehensive genetic analyses, partitioned into human leukocyte antigen (HLA) and non-HLA variations. Using FinnGen's 12,563 trios, a within-family polygenic transmission analysis demonstrated that the aggregation of many parental AIDs with offspring T1D can be partially explained by HLA and non-HLA polymorphisms in a disease-dependent manner. We therefore proposed a parental polygenic score (PGS), incorporating both HLA and non-HLA polymorphisms, to characterize the cumulative risk pattern of T1D in offspring. This raises an intriguing possibility of using parental PGS, in conjunction with clinical diagnoses, to inform individuals about T1D risk in their offspring.
2. Impact of maternal anemia during pregnancy on neonatal metabolic profiles: evidence from the Beijing Birth Cohort Study.
In a prospective cohort of 12,116 pregnancies, maternal anemia was linked to higher rates of abnormal neonatal metabolic screens, decreased alanine/arginine, increased tyrosine, and broad reductions in acylcarnitines (except elevated C5), implicating impaired fatty acid beta-oxidation. Sensitivity analyses in normal birth weight and term infants confirmed robustness.
Impact: Links a common, modifiable maternal condition to neonatal metabolic pathway perturbations at scale, offering mechanistic insight and potential targets for perinatal intervention.
Clinical Implications: Enhanced screening and treatment of gestational anemia may reduce false-positive newborn screens and mitigate metabolic vulnerabilities related to fatty acid oxidation in neonates.
Key Findings
- Metabolic abnormalities were higher in neonates of anemic mothers vs controls: overall 20.83% vs 16.1%, amino acid 11.9% vs 9.25%, acylcarnitine 11.11% vs 8.04% (P<0.05).
- Specific amino acid changes: alanine and arginine decreased, tyrosine increased in the anemia group.
- Most acylcarnitines (e.g., C0, C2, C10, C12, C14, C16, C18 species) were reduced, except elevated C5; pathway analysis implicated fatty acid beta-oxidation and related pathways.
- Sensitivity analyses in normal birth weight and term infants reproduced the main findings; citrulline and arginine decreases were linked to aspartate metabolism and the urea cycle.
Methodological Strengths
- Large prospective cohort (n=12,116) with standardized newborn screening measures
- Pathway analysis to contextualize metabolite changes
- Robustness tested via sensitivity analyses in key subgroups (NBW and term infants)
Limitations
- Observational design limits causal inference
- Anemia severity, iron supplementation, and other clinical interventions were not detailed in the abstract
- Clinical significance of specific metabolite shifts requires longitudinal follow-up
Future Directions: Track long-term neurodevelopmental and metabolic outcomes, evaluate whether treating maternal anemia normalizes neonatal metabolomics, and explore mechanisms linking iron status to fatty acid oxidation.
BACKGROUND: Anemia during pregnancy is associated with various adverse neonatal outcomes. However, the association between maternal anemia during pregnancy and newborn metabolic profiles remains unclear. This study aimed to investigate whether anemia during pregnancy is associated with alterations in neonatal metabolic profiles. METHODS: This prospective observational cohort study included 12,116 pregnant women, with or without gestational anemia, recruited through the Beijing Birth Cohort Study (ChiCTR2200058395), along with their neonates born between July 2021 and October 2022 in Beijing, China. RESULTS: Among the 12,116 participants, 576 pregnant women were diagnosed with anemia (Anemia group), while 11,540 did not have anemia (Control group). The rates of metabolic profile abnormalities were significantly higher in the Anemia group compared to the Control group (P < 0.05): 20.83% vs. 16.1% for the overall metabolic profile, 11.9% vs. 9.25% for amino acid profiles, and 11.11% vs. 8.04% for acylcarnitine profiles. Individual metabolic indicators showed significant differences: alanine and arginine levels significantly decreased, while tyrosine levels significantly increased in the Anemia group. Notably, most acylcarnitines indicators (C0, C2, C4DC + C5-OH, C5DC + C6-OH, C6, C6DC, C10, C10:1, C12, C12:1, C14, C14:1, C14:2, C16, C16:1, C16:1-OH, C18, and C18:1) were significantly reduced in the Anemia group, except for C5, which was elevated. Pathway analysis revealed that these alterations were associated with beta-oxidation of very long-chain fatty acids, oxidation of branched-chain fatty acids, mitochondrial beta-oxidation of long-chain saturated fatty acids, and fatty acid metabolism. All of these pathways were related to fatty acid oxidation. Sensitive analyses in normal birth weight (NBW) and term infants (TI) confirmed these findings and demonstrated their robustness. In addition, in NBW infants and TIs, citrulline and arginine were significantly decreased, which were associated with aspartate metabolism and the urea cycle. CONCLUSIONS: Maternal anemia during pregnancy is significantly associated with alterations in neonatal metabolic profiles, particularly in fatty acid beta-oxidation and related pathways. These findings highlight the potential metabolic consequences of gestational anemia and provide insights into its role in adverse neonatal outcomes and abnormal newborn screening results.
3. Dietary misreporting: a comparative study of recalls vs energy expenditure and energy intake by doubly-labeled water in older adults with overweight or obesity.
Comparing two DLW-anchored approaches, the novel energy-balance method flagged more over-reported recalls (23.7% vs 10.2%) and achieved greater bias reduction, while under-reporting remained ~50% by both methods. Relationships between energy intake and anthropometrics emerged only after filtering to plausible reports.
Impact: Provides a practical, more accurate framework for classifying plausible dietary recalls using criterion measures (DLW and energy balance), crucial for obesity and diabetes research relying on self-report.
Clinical Implications: Improved identification of plausible energy intake reports can reduce bias in nutrition trials and epidemiology, strengthening dietary guidance and interventions in endocrine/metabolic diseases.
Key Findings
- Under-reporting prevalence was 50% with both the standard (rEI:mEE) and novel (rEI:mEI) methods.
- The novel method classified more over-reported recalls (23.7%) and fewer plausible recalls (26.3%) compared with the standard method (over-reported 10.2%, plausible 40.3%).
- Measured energy intake correlated with weight (β=21.7, p<0.01) and BMI (β=48.8, p=0.04), whereas rEI did not until filtering to plausible entries.
- Bias reduction was greater with the novel method (remaining bias for weight 24.9% vs 49.5%; for BMI 56.9% vs 60.2%).
Methodological Strengths
- Use of DLW as criterion for mEE and energy-balance–derived mEI as a second criterion
- Predefined cutoffs with variability propagation and formal bias estimation (bβ, dβ)
- Agreement assessed with kappa statistics and relationships tested via regression
Limitations
- Sample size and setting are not stated in the abstract, limiting immediate assessment of generalizability
- Older adults with overweight/obesity may not represent other populations
- Method performance against alternative objective measures beyond DLW/energy balance was not examined
Future Directions: Validate the energy-balance method across diverse cohorts and dietary patterns, and integrate it into trial workflows and registries to predefine plausibility thresholds.
BACKGROUND: Self-report methods are widely used to assess energy intake but are prone to measurement errors. We aimed to identify under-reported, over-reported, and plausible self-reported energy intake by dietary recalls (rEI) using a standard method (Method 1) that calculates the rEI ratio against measured energy expenditure (mEE) by doubly-labeled water (DLW), and compare it to a novel method (Method 2), which calculates the rEI ratio against measured energy intake (mEI) by the principle of energy balance (EB = mEE + changes in energy stores). METHODS: The rEI:mEE and rEI:mEI ratios were assessed for each subject. Group cut-offs were calculated for both methods, using the coefficient of variations of rEI, mEE, and mEI. Entries within ± 1SD of the cutoffs were categorized as plausible, < 1SD as under-reported, and > 1SD as over-reported. Kappa statistics was calculated to assess the agreement between both methods. Percentage bias (bβ) was estimated by linear regression. Remaining bias (dβ) was calculated after applying each method cutoffs. RESULTS: The percentage of under-reporting was 50% using both methods. Using Method 1, 40.3% of recalls were categorized as plausible, and 10.2% as over-reported. With Method 2, 26.3% and 23.7% recalls were plausible and over-reported, respectively. There was a significant positive relationship between mEI with weight (ß = 21.7, p < 0.01) and BMI (ß = 48.8, p = 0.04), but not between rEI with weight (ß = 13.1, p = 0.06) and BMI (ß = 41.8, p = 0.11). The rEI relationships were significant when only plausible entries were included using Method 1 (weight: ß = 17.4, p < 0.01, remaining bias = 49.5%; BMI: ß = 44.6, p = 0.01, remaining bias = 60.2%) and Method 2 (weight: ß = 19.5, p < 0.01, remaining bias = 24.9%; BMI: ß = 44.8, p = 0.03, remaining bias = 56.9%). CONCLUSIONS: The choice of method significantly impacts plausible and over-reported classification, with the novel method identifying more over-reported entries. While rEI showed no relationships with anthropometric measurements, applying both methods reduced bias. The novel method showed greater bias reduction, suggesting that it may have superior performance when identifying plausible rEI. CLINICAL TRIALS REGISTRATION: NCT04465721.