Evidence map›Paper›PMID 38849738›Full record

Observational studyBMC pregnancy and childbirth2024

Investigating the association between metabolic syndrome conditions and perinatal mental illness: a national administrative claims study.

Karishma Chhabria, Sudhakar Selvaraj, Jerrie Refuerzo, Chau Truong, Cecilia Ganduglia Cazaban

Abstract readObservational Study
In one paragraph

Observational study in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Karishma Chhabria *Division of Management Policy and Community Health, Center for Healthcare Data Research, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA. kchhabria@uwf.edu.
Sudhakar Selvaraj *Louis Faillace, MD, Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA.
Jerrie RefuerzoDivision of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Chau TruongDivision of Management Policy and Community Health, Center for Healthcare Data Research, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA.
Cecilia Ganduglia CazabanDivision of Management Policy and Community Health, Center for Healthcare Data Research, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough the association between mental disorder and metabolic syndrome as a bidirectional relationship has been demonstrated, there is little knowledge of the cumulative and individual effect of these conditions on peripartum mental health. This study aims to investigate the association between metabolic syndrome conditions (MetS-C) and maternal mental illness in the perinatal period, while exploring time to incident mental disorder diagnosis in postpartum women.

methodsThis observational study identified perinatal women continuously enrolled 1 year prior to and 1 year post-delivery using Optum's de-identified Clinformatics® Data Mart Database (CDM) from 2014 to 2019 with MetS-C i.e. obesity, diabetes, high blood pressure, high triglycerides, or low HDL (1-year prior to delivery); perinatal comorbidities (9-months prior to and 4-month postpartum); and mental disorder (1-year prior to and 1-year post-delivery). Additionally, demographics and the number of days until mental disorder diagnosis were evaluated in this cohort. The analysis included descriptive statistics and multivariable logistic regression. MetS-C, perinatal comorbidities, and mental disorder were assessed using the International Classification of Diseases, Ninth, and Tenth Revision diagnosis codes.

results372,895 deliveries met inclusion/exclusion criteria. The prevalence of MetS-C was 13.43%. Multivariable logistic regression revealed prenatal prevalence (1.64, CI = 1.59-1.70) and postpartum incident (1.30, CI = 1.25-1.34) diagnosis of mental health disorder were significantly higher in those with at least one MetS-C. Further, the adjusted odds of having postpartum incident mental illness were 1.51 times higher (CI = 1.39-1.66) in those with 2 MetS-C and 2.12 times higher (CI = 1.21-4.01) in those with 3 or more MetS-C. Young women (under the age of 18 years) were more likely to have an incident mental health diagnosis as opposed to other age groups. Lastly, time from hospital discharge to incident mental disorder diagnosis revealed an average of 157 days (SD = 103 days).

conclusionThe risk of mental disorder (both prenatal and incident) has a significant association with MetS-C. An incremental relationship between incident mental illness diagnosis and the number of MetS-C, a significant association with younger mothers along with a relatively long period of diagnosis mental illness highlights the need for more screening and treatment during pregnancy and postpartum.

Indexed as

Mental DisordersMetabolic SyndromePregnancy ComplicationsAdultComorbidityDatabases, FactualFemaleHumansPeripartum PeriodPostpartum PeriodPregnancyPrevalenceUnited StatesYoung AdultAdministrative claims dataMaternal mental healthMetabolic syndromePerinatal comorbidities

Identifiers

PMID38849738
PMCPMC11157911

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.