When first line therapy with metformin is insufficient for patients with type 2 diabetes (T2D), the optimal adjunctive therapy is unclear. We assessed the efficacy and safety of adjunctive antidiabetic agents in patients with inadequately controlled T2D on metformin alone.clinicaltrials.gov, regulatory websites was performed. We included randomized controlled trials of 3–12 months duration, evaluating Food and Drug Administration or European Union approved agents  in patients experiencing inadequate glycemic control with metformin monotherapy . Random-effects network meta-analyses were used to compare the weighted mean difference for changes from baseline in HbA1c, body weight (BW) and systolic blood pressure (SBP), and the risk of developing hypoglycemia, urinary (UTI) and genital tract infection (GTI).A search of MEDLINE and CENTRAL, Sixty-two trials evaluating 25 agents were included. All agents significantly reduced HbA1c vs. placebo; albeit not to the same extent . Glargine, sulfonylureas (SUs) and nateglinide were associated with increased hypoglycemia risk vs. placebo . Sodium glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 analogs, miglitol and empagliflozin/linagliptin significantly reduced BW  whereas SUs, thiazolindinediones, glargine and alogliptin/pioglitazone caused weight gain . SGLT2 inhibitors, empagliflozin/linagliptin, liraglutide and sitagliptin decreased SBP . No therapy increased UTI risk vs. placebo; however, SGLT2 inhibitors were associated with an increased risk of GTI .Adding different AHAs to metformin was associated with varying effects on HbA1c, BW, SBP, hypoglycemia, UTI and GTI which should impact clinician choice when selecting adjunctive therapy. The American Diabetes Association (ADA) and European Association for the Study of Diabetes (EASD) recommend lifestyle modifications and metformin as first-line therapy in type 2 diabetes mellitus (DM) . HoweverTraditional pair-wise meta-analysis can be used to evaluate the efficacy and safety of two drugs based on evidence from RCTs that directly compare them. However, in absence of such direct head-to-head comparisons, network meta-analysis (NMA) provides a statistical framework that incorporates evidence from both direct and indirect comparisons from a network of studies of different therapies and evaluates their relative treatment effects –4.We performed a NMA to assess the comparative efficacy and safety of adjunctive antidiabetic medication therapies in patients with Type 2 DM not adequately controlled on stable and optimized metformin monotherapy.www.clinicaltrials.gov, regulatory agency reports and by contacting investigators for clarification or additional data. Two investigators reviewed all potentially relevant citations independently .We performed a systematic literature search for all relevant articles from the earliest date through May 2014 in MEDLINE and Cochrane CENTRAL. The search strategy combined the Medical Subject Heading (MeSH) and keywords for “metformin” with terms for Type 2 DM and for glycosylated hemoglobin A1c (HbA1c). Our MEDLINE search strategy is included in To be included, studies had to: (1) be published in English; (2) utilize a parallel RCT design (any phase) in adults (≥18 years) with Type 2 DM; (3) compare Food and Drug Administration (FDA) or European Union (EU)-approved antidiabetic drug therapy including non-insulin and long-acting, once-daily basal insulin agents (as a single or combination adjunctive therapy) to another antidiabetic therapy or placebo (in addition to metformin); 4) include only patients who showed inadequate response to stable, optimized metformin monotherapy at randomization; (5) treat patients for 12 to 52 glycemic weeks after randomization; and (6) report change in HbA1c from baseline (our primary endpoint). As in previous NMAs [ include Validity assessment was performed by 2 investigators  independently using the Cochrane Risk of Bias Tool . This chTwo investigators  used a standardized tool to independently extract all data with disagreements resolved by discussion or a third investigator (CIC). The following information was sought from each trial: (1) author identification; 2) year of publication; (3) study design and methodological quality information needed to complete the Cochrane Collaboration’s tool for assessing risk of bias  year of ; (4) sam2 statistic with a value >50% representing important statistical heterogeneity) and publication bias (using the Egger’s weighted regression statistic with a P <0.05 suggesting a higher likelihood of publication bias) were assessed.We performed traditional meta-analyses analyzing changes in HbA1c, BW and SBP as continuous variables using StatsDirect version 2.7.8  with a P<0.05 considered statistically significant. Separate pair-wise analyses were performed for each antidiabetic therapy, combining data from approved doses of the same therapies using the method recommended by the Cochrane Collaboration [We then performed NMA, a generalization of traditional pairwise meta-analysis that compares all pairs of treatments within a set of treatments for the same disease state (in this case Type 2 DM) . Along wThe literature search yielded 1,005 citations, and we identified 17 additional RCTs through other sources . After rCharacteristics of the included trials are described in The SGLT2 inhibitors had similar effects on reducing HbA1c when compared to placebo in the NMA, ranging from 0.48% for dapagliflozin to 0.72% for canagliflozin . When coBoth combination agents were associated with significant reductions in HbA1c when compared to placebo in the NMA . When comparing the combination medications to the other active single agents, alogliptin/pioglitazone significantly reduced HbA1c when compared to all other therapies except for insulin glargine, glibenclamide and repaglinide; whereas empagliflozin/linagliptin was more efficacious when compared to all other active single agents except for insulin glargine, glibenclamide, repaglinide and acarbose.In terms of clinical superiority (lower bound of the 95%CI depicted an HbA1c reduction greater than 0.3%) alogliptin/pioglitazone and empagliflozin/linagliptin were clinically superior to 52% and 24% of the other antidiabetic medications analyzed, respectively. Alogliptin/pioglitazone was clinically superior to all DPP-4 inhibitors, colesevelam, dapagliflozin, glipizide, lixisenatide, miglitol, nataglinide, empagliflozin and pioglitazone. Empagliflozin/linagliptin was clinically superior to canagliflozin, dapagliflozin, glipizide, miglitol, nateglinide and saxagliptin.All antidiabetic agents were associated with statistically significant reductions in HbA1c relative to placebo, ranging from 0.43% for miglitol to 1.29% for glibenclamide . When coAlong with statistical significance, all therapies were found clinically superior to placebo (lower bound of the 95%CI depicted an HbA1c reduction greater than 0.3%) except for colesevelam, nateglinide and miglitol . In head-to-head comparisons, and insulin glargine was clinically superior to 40% of the other antidiabetic medications analyzed. Insulin glargine was found to be clinically superior to all DPP-4 inhibitors, colesevelam, dapagliflozin, glipizide, lixisenatide, miglitol, and nataglinide.All SGLT2 inhibitors were associated with significant weight loss when compared to placebo in the NMA (range: 2.08–2.17 kg) . When coAlogliptin/pioglitazone was associated with significant increases in BW when compared with placebo in the NMA  whereas empagliflozin/linagliptin was associated with significant weight loss . When compared to other single active agents, empagliflozin/linagliptin was associated with significant weight loss compared to all other agents except SGLT-2 inhibitors, and GLP-1 analogs. Alogliptin/pioglitazone caused significant increases in BW compared to all other active agents except SUs, repaglinide and TZDs. In terms of clinically superior weight gain, (lower bound of the 95%CI depicted a decrease in weight less than 2.3 kg), alogliptin/pioglitazone was associated with clinically superior weight gain compared to SGLT2 inhibitors, empagliflozin/linagliptin, GLP-1 analogs, and miglitol (range: 3.54–4.65 kg).The SUs, TZDs, insulin glargine and repaglinide were associated with significant increases in BW when compared with placebo in the NMA (range: 1.19–2.44 kg). GLP-1 analogs and miglitol were associated with significant weight loss (range: 1.15–2.26 kg) but there was no weight change with acarbose, any DPP-4 inhibitor, colesevelam and nateglinide when compared to placebo. When comparing active agents, GLP-1 analogs were associated with statistically greater weight loss when compared to all other agents except SGLT2 inhibitors and miglitol. While several agents exhibited statistically significant weight loss, no agent demonstrated clinically superior weight loss compared to placebo (lower bound of the 95%CI depicted a decrease in weight less than 2.3 kg). When comparing the clinical superiority of single active agents, TZDs were associated with clinically superior weight gain when compared to GLP-1 analogs (range: 3.22–4.41 kg).All SGLT2 inhibitors were associated with a decrease in SBP compared with placebo in the NMA  (range: 4.4–5.64 mmHg), and saxagliptin and sitagliptin (range: 2.26–5.79 mmHg) . No SGLTEmpagliflozin/linagliptin was associated with a decrease in SBP when compared with placebo in the NMA . In head-to-head comparisons, empagliflozin/linagliptin significantly reduced SBP when compared to SUs, linagliptin, saxagliptin and sitagliptin; however it did not show clinical superiority compared to any other active agents. There were no data to evaluate alogliptin/pioglitazone for this endpoint.Liraglutide  and sitagliptin  were associated with a decrease in SBP compared with placebo. No medication showed clinical superiority (lower bound of the 95%CIs depicted a decrease in SBP less than 5 mmHg) compared to placebo or another active agent; however, there were no data to evaluate 12 (48%) of the agents for this endpoint.Upon NMA, the SGLT2 inhibitors were not associated with an increased risk of confirmed hypoglycemia compared with placebo . In the Empagliflozin/linagliptin was not associated with increased risk of hypoglycemia compared with placebo in the NMA . In the active drug comparisons, insulin glargine, nateglinide, both meglitinides and all SUs were associated with significantly higher rates of confirmed hypoglycemia compared to empagliflozin/linagliptin . There were no data to evaluate alogliptin/pioglitazone for this endpoint.Insulin glargine, all SUs, and nateglinide were associated with significantly higher rates of confirmed hypoglycemia compared with placebo upon NMA  . All GLPNo treatment was associated with an increased risk of UTI when compared to placebo; however, there were no data to evaluate 13 (52%) of the agents for this endpoint . NMA sugResults from all pair-wise traditional meta-analyses for each of the six endpoints are reported in Results from the sensitivity analysis are reported in When HbA1c goals are not met or maintained with lifestyle modifications and metformin monotherapy, the ADA/EASD guidelines recommend patients initiate an additional antidiabetic agent from 1 of 5 classes  but do not endorse specific agents in the overall Type 2 DM population or specific patient subtypes [Our HbA1c results showed both statistical differences and clinical superiority between antidiabetic therapies. All therapies significantly reduced HbA1c, but to differing degrees when compared to placebo. Combination therapies  and insulin glargine were statistically and clinically superior in reducing HbA1c compared to a majority of other antidiabetic agents. As a class, the SGLT2 inhibitors were similar in efficacy to other non-insulin monotherapies recommended by the ADA as add-ons to metformin, which warrants an update to clinical practice guidelines to include them as a treatment option. Since the FDA uses a 0.3–0.4% noninferiority margin to assess clinical comparative efficacy, colesevelam, nateglinide and miglitol were not clinically superior to placebo  making them less attractive as adjunctive therapies .A therapy’s efficacy, or ability to lower HbA1c, must be weighed against a therapy’s propensity to cause hypoglycemia. In fact, guidelines emphasize the importance of preventing even mild hypoglycemia . InsulinThe majority of patients with Type 2 DM are overweight which may have notable impacts on insulin resistance, glycemic control and cardiovascular risk . SUs, ina priori definition of clinical superiority ; however, incoherence cannot be completely ruled out. The eight statistically significant findings resulting from 151 statistical tests are consistent with 7–8 findings that would be expected due to chance alone. We only included English-language RCTs, but due to the vast number of trials included in our meta-analysis, a limited number of trials missed for this reason would likely have minimal impact on our results. We found sparse reporting of certain endpoints (UTI and GTI) which makes it difficult to draw concrete conclusions . At the same time, our NMA cross- compared 25 therapies plus placebo on 6 different endpoints; thus multiple hypothesis testing can lead to erred conclusions of statistical differences between therapies when they do not truly exist (type 1 error). However, we conducted a sensitivity analysis using 99% CIs as a more stringent cutoff for statistical significance and found the majority of comparisons to still be statistically significant. Other agent-specific endpoints  could not be assessed due to the limited data and short follow-up periods of the included trials. Lastly, publication bias is always a concern in a meta-analysis, but we minimized this concern with our systematic search strategy, backwards citation tracking and multi-pronged grey literature searches.In persons with Type 2 DM inadequately-controlled on stable metformin monotherapy, all agents analyzed in our NMA proved effective (lowered HbA1c vs. placebo) but with differing reductions in HbA1c. Evaluated agents differed in their effects on BW, SBP, hypoglycemia and GTI; however no medication increased the risk of UTI when compared to placebo. The newest class of antidiabetic agents, the SGLT2 inhibitors, was found to provide similar HbA1c efficacy to other non-insulin monotherapies  with the added benefits of weight loss, reduced SBP and a low risk of hypoglycemia; but at a cost of an increased risk of GTI. Combination therapies resulted in some of the largest reductions in HbA1c and may be appropriate for patients requiring profound (>1%) HbA1c reductions after failing optimized metformin.S1 Checklist(DOC)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S2 AppendixGTI = genital tract infection; HbA1c = glycated hemoglobin; IVRS = interactive voice response system; SGLT2 = sodium glucose co-transporter-2; SU = sulfonylurea; TZD = thiazolidinedione; UTI = urinary tract infection.(PDF)Click here for additional data file.S1 FigIn this figure (x) is defined as the minimally important clinical difference between two treatments (or a treatment and placebo). Using the example of HbA1c reduction, line A displays an HbA1c difference between therapies that is not statistically significant because the 95% confidence interval (CI) cross the line of no effect; lines B and C both show drug A to be statistically significantly better at lowering HbA1c than drug B, but that clinical superiority cannot be claimed in either case because the lower bound of the 95% CI crosses the (dashed) line marking the minimally important clinical difference, (MICD) x ; line D depicts a situation where drug A is both statistically significant and clinically superior in reducing HbA1c compared to drug B as evidenced by the lower bound of the 95%CI not crossing the line marking the MICD. In our network meta-analysis, the comparison of empagliflozin/linagliptin vs. dapagliflozin  would met these criteria for statistical significance and clinical superiority. MICD = minimally important clinical difference; RR = relative risk.(PDF)Click here for additional data file.S2 Fig+ = low risk of bias;? = unclear risk of bias;– = high risk of bias.(PDF)Click here for additional data file.S3 FigTherapies are reported in alphabetical order. HbA1c results are reported in WMD, % (95% CI). Results for changes in HbA1c on the top portion of the matrix represent changes in the row-defining treatment vs. those in the column-defining treatment (referent). For changes in HbA1c, negative values favor the first agent in alphabetical order. Statistically significant results are bolded. Clinically superior results are underlined. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. The results on the bottom portion of the matrix represent the reciprocal of the top portion. ACA = acarbose; ALO = alogliptin; ALO/PIO = alogliptin/pioglitazone; CANA = canagliflozin; COL = colesevelam; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = glargine; GLIB = glibenclamide; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; HbA1c = hemoglobin A1c; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; MIG = miglitol; NAT = nateglinide; PIO = pioglitazone; PLC = placebo; REP = repaglinide; ROSI = rosiglitazone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S4 FigTherapies are reported in alphabetical order. Results are reported in WMD, kg (95% CI). Results for changes in weight on the top portion of the matrix represent changes in the row-defining treatment vs. those in the column-defining treatment (referent). For changes in weight, negative values favor the first agent in alphabetical order. Statistically significant results are bolded. Clinically superior results are underlined. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. The results on the bottom portion of the matrix represent the reciprocal of the top portion. ACA = acarbose; ALO = alogliptin; ALO/PIO = alogliptin/pioglitazone; CANA = canagliflozin; COL = colesevelam; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = glargine; GLIB = glibenclamide; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; MIG = miglitol; NAT = nateglinide; PIO = pioglitazone; PLC = placebo; REP = repaglinide; ROSI = rosiglitazone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S5 FigTherapies are reported in alphabetical order. Results are reported in WMD, mmHg (95% CI). Results for changes in systolic blood pressure (SBP) on the top portion of the matrix represent changes in the row-defining treatment vs. those in the column-defining treatment (referent). For changes in SBP, negative values favor the first agent in alphabetical order. Statistically significant results are bolded. Clinically superior results are underlined. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. The results on the bottom portion of the matrix represent the reciprocal of the top portion. CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; PIO = pioglitazone; PLC = placebo; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S6 FigTherapies are reported in alphabetical order. Results for risk of confirmed hypoglycemia on the top portion of the matrix represent relative risks (RRs) of hypoglycemia in the row-defining treatment vs. those the column-defining treatment (referent). For confirmed hypoglycemia, RRs lower than 1 favor the first agent in alphabetical order. Statistically significant results are bolded. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. To obtain RRs for comparisons in the opposite direction, reciprocals should be taken or the lower portion of the matrix can be used. ACA = acarbose; ALO = alogliptin; CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = glargine; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; NAT = nateglinide; PIO = pioglitazone; PLC = placebo; REP = repaglinide; ROSI = rosiglitazone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S7 FigTherapies are reported in alphabetical order. Results for risk of urinary tract infection (UTI) on the top portion of the matrix represent relative risks (RRs) of UTI in the row-defining treatment vs. those the column-defining treatment (referent). For UTI, RRs lower than 1 favor the first agent in alphabetical order. Statistically significant results are bolded. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. To obtain RRs for comparisons in the opposite direction, reciprocals should be taken or the lower portion of the matrix can be used. ALO/PIO = alogliptin/pioglitazone; ALO = alogliptin; CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; PIO = pioglitazone; PLC = placebo; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S8 FigTherapies are reported in alphabetical order. Results for risk of genital tract infection (GTI) on the top portion of the matrix represent relative risks (RRs) of GTI in the row-defining treatment vs. those the column-defining treatment (referent). For GTI, RRs lower than 1 favor the first agent in alphabetical order. Statistically significant results are bolded. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. To obtain RRs for comparisons in the opposite direction, reciprocals should be taken or the lower portion of the matrix can be used. CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; PLC = placebo; SITA = sitagliptin.(PDF)Click here for additional data file.S9 FigTherapies are reported in alphabetical order. HbA1c results are reported in WMD, % (99% CI). Results for changes in HbA1c on the top portion of the matrix represent changes in the row-defining treatment vs. those in the column-defining treatment (referent). For changes in HbA1c, negative values favor the first agent in alphabetical order. Statistically significant results of the sensitivity analysis are colored grey. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. The results on the bottom portion of the matrix represent the reciprocal of the top portion. ACA = acarbose; ALO = alogliptin; ALO/PIO = alogliptin/pioglitazone; CANA = canagliflozin; COL = colesevelam; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = glargine; GLIB = glibenclamide; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; HbA1c = hemoglobin A1c; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; MIG = miglitol; NAT = nateglinide; PIO = pioglitazone; PLC = placebo; REP = repaglinide; ROSI = rosiglitazone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S10 FigTherapies are reported in alphabetical order. Results are reported in WMD, kg (95% CI). Results for changes in weight on the top portion of the matrix represent changes in the row-defining treatment vs. those in the column-defining treatment (referent). For changes in weight, negative values favor the first agent in alphabetical order. Statistically significant results of the sensitivity analysis are colored grey. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. The results on the bottom portion of the matrix represent the reciprocal of the top portion. ACA = acarbose; ALO = alogliptin; ALO/PIO = alogliptin/pioglitazone; CANA = canagliflozin; COL = colesevelam; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = glargine; GLIB = glibenclamide; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; MIG = miglitol; NAT = nateglinide; PIO = pioglitazone; PLC = placebo; REP = repaglinide; ROSI = rosiglitazone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S11 FigTherapies are reported in alphabetical order. Results are reported in WMD, mmHg (95% CI). Results for changes in systolic blood pressure (SBP) on the top portion of the matrix represent changes in the row-defining treatment vs. those in the column-defining treatment (referent). For changes in SBP, negative values favor the first agent in alphabetical order. Statistically significant results of the sensitivity analysis are colored grey. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. The results on the bottom portion of the matrix represent the reciprocal of the top portion. CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; PIO = pioglitazone; PLC = placebo; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S12 FigTherapies are reported in alphabetical order. Results for risk of confirmed hypoglycemia on the top portion of the matrix represent relative risks (RRs) of hypoglycemia in the row-defining treatment vs. those the column-defining treatment (referent). For confirmed hypoglycemia, RRs lower than 1 favor the first agent in alphabetical order. Statistically significant results of the sensitivity analysis are colored grey. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. To obtain RRs for comparisons in the opposite direction, reciprocals should be taken or the lower portion of the matrix can be used. ACA = acarbose; ALO = alogliptin; CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = glargine; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; NAT = nateglinide; PIO = pioglitazone; PLC = placebo; REP = repaglinide; ROSI = rosiglitazone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S13 FigTherapies are reported in alphabetical order. Results for risk of urinary tract infection (UTI) on the top portion of the matrix represent relative risks (RRs) of UTI in the row-defining treatment vs. those the column-defining treatment (referent). For UTI, RRs lower than 1 favor the first agent in alphabetical order. Statistically significant results of the sensitivity analysis are colored grey. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. To obtain RRs for comparisons in the opposite direction, reciprocals should be taken or the lower portion of the matrix can be used. ALO/PIO = alogliptin/pioglitazone; ALO = alogliptin; CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; PIO = pioglitazone; PLC = placebo; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S14 FigTherapies are reported in alphabetical order. Results for risk of genital tract infection (GTI) on the top portion of the matrix represent relative risks (RRs) of GTI in the row-defining treatment vs. those the column-defining treatment (referent). For GTI, RRs lower than 1 favor the first agent in alphabetical order. Statistically significant results of the sensitivity analysis are colored grey. Sodium glucose co-transporter-2 (SGLT-2) inhibitors are highlighted. To obtain RRs for comparisons in the opposite direction, reciprocals should be taken or the lower portion of the matrix can be used. CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; PLC = placebo; SITA = sitagliptin.(PDF)Click here for additional data file.S1 TableaNumber of study participants evaluated for change in A1c from baseline (sample size may vary for other endpoints). bDoses were given daily, unless otherwise specified. cData given in median (range).BID = twice daily; BMI = body mass index; DM = diabetes mellitus; FPG = fasting plasma glucose; HbA1c = hemoglobin A1c; n = number of patients; NR = not reported; OAD = oral antidiabetic drug; SBP = systolic blood pressure; SU = sulfonylurea; SE = standard error; TID = three times a day; qAM = daily AM dosing; qPM = daily PM dosing; QW = weekly. (PDF)Click here for additional data file.S2 TableaI2>50%.ACA = acarbose; ALO = alogliptin; ALO/PIO = alogliptin/pioglitazone; CANA = canagliflozin; COL = colesevelam; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = insulin glargine; GLIB = glibenclamide; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; MIG = miglitol; NAT = nateglinide; PIO = pioglitizone; PLC = placebo; REP = repaglinide; ROSI = rosiglitizone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin. (PDF)Click here for additional data file.S3 TableaI2>50%.ACA = acarbose; ALO = alogliptin; ALO/PIO = alogliptin/pioglitazone; CANA = canagliflozin; COL = colesevelam; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = insulin glargine; GLIB = glibenclamide; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; MIG = miglitol; NAT = nateglinide; PIO = pioglitizone; PLC = placebo; REP = repaglinide; ROSI = rosiglitizone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin. (PDF)Click here for additional data file.S4 TableaEgger’s p-value <0.05CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = insulin glargine; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; PIO = pioglitizone; PLC = placebo; SAX = saxagliptin; SBP = systolic blood pressure; SITA = sitagliptin; VILDA = vildagliptin. (PDF)Click here for additional data file.S5 TableACA = acarbose; ALO = alogliptin; CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; EXEN = exenatide; GLAR = insulin glargine; GLIC = gliclazide; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; LIRA = liraglutide; LIX = lixisenatide; NAT = nateglinide; PIO = pioglitizone; PLC = placebo; REP = repaglinide; ROSI = rosiglitizone; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S6 TableALO = alogliptin; ALO/PIO = alogliptin/pioglitazone; CANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; PIO = pioglitizone; PLC = placebo; SAX = saxagliptin; SITA = sitagliptin; VILDA = vildagliptin.(PDF)Click here for additional data file.S7 TableCANA = canagliflozin; DAPA = dapagliflozin; EMPA = empagliflozin; EMPA/LINA = empagliflozin/linagliptin; GLIM = glimepiride; GLIP = glipizide; LINA = linagliptin; PLC = placebo; SITA = sitagliptin.(PDF)Click here for additional data file.


P. falciparum or P. vivax malaria annually. The association between microscopically patent and PCR positive sub-microscopic malaria with maternal anaemia and low birth weight babies was assessed in South-west Sumba and Jayapura district, Papua, Indonesia.Malaria in pregnancy poses a major public health problem in Indonesia with an estimated 6 million pregnancies at risk of Cross sectional surveys were conducted between June 2008-May 2009 in antenatal women and at delivery including home deliveries in SW Sumba (low-moderate transmission) and Jayapura (high transmission). Maternal and placental blood samples were obtained at antenatal attendance and at delivery for haemoglobin measurement, smear microscopy and PCR.P. falciparum: 58.3%, with P. vivax: 20.6% and 0.5% P. ovale and 5.6% mixed infections , and this was 12.3% by PCR : 10.1%) and 10.7% in Sumba and 14.2% in Jayapura, respectively. Sub-microscopic parasitaemia  was detected in 8.3% women. With patent infections only P. falciparum was associated with moderate-severe anaemia (Hb ≤9 g/dL); [RR 2.2 (95% CI 1.7-2.9) P. vivax RR 0.5 (0.3-0.9)] whereas with sub-microscopic infections both species were associated with increased risk of moderate to severe anaemia [P. falciparum: RR 2.1 (95% CI 1.6-2.8) and P. vivax: RR 1.9 (95% CI 1.3-2.7)]. The prevalence of placental malaria was 4.8% (72/1632) by PCR and 3.3% sub-microscopically; this was 2.4% in primigravidae, 3.1% in secundi and 1.7% in multigravidae (≥3 pregnancies). The mean difference in birth weight in women with patent placental P. falciparum infection compared with uninfected women was 101 g (95% CI 70-273 g) with RR 2.1 (95% CI 1.2-3.0) for low birth weight. The reduction in birth weight with sub-microscopic infection was 70 g (63-204 g) with low birth weight RR 1.7 (95% CI 1.0-2.5). Corresponding figures for P. vivax were: patent placental infections: 252 g (57-560 g), low birth weight RR 4.7 (95% CI 2.2-67); and for sub-microscopic infections the mean difference in birth weight was 146 (70-364 g); low birth weight RR 2.9 (95% CI 1.6-3.4).A total of 4230 women  were enrolled. The prevalence of maternal parasitaemia detected by microscopy was 7.0% [(180/2598) (mono-infection with Sub-microscopic infections are common in eastern Indonesia and are associated with maternal anaemia and marked reductions in birth weight. Strategies that prevent sub-microscopic infections such as intermittent preventing therapy or that which results in their early detection and treatment, such as intermittent screening strategies should now be explored in South-east Asia.


Studies suggest that arsenic exposure influences birth outcomes; however, findings are mixed.We assessed in utero arsenic exposure in relation to birth outcomes and whether maternal prepregnancy weight and infant sex modified the associations.Among 706 mother–infant pairs exposed to low levels of arsenic through drinking water and diet, we assessed in utero arsenic exposure using maternal second-trimester urinary arsenic, maternal prepregnancy weight through self-report, and birth outcomes from medical records.Median (interquartile range) of total urinary arsenic  was 3.4 μg/L (1.7–6.0). In adjusted linear models, each doubling of tAs was associated with a 0.10-cm decrease  in head circumference. Results were similar for MMA and DMA. Ln(tAs) and ln(DMA) were positively associated with birth length in infant males only; among males, each doubling of tAs was associated with a 0.28-cm increase  in birth length (pinteraction = 0.04). Results were similar for DMA. Additionally, arsenic exposure was inversely related to ponderal index, and associations differed by maternal weight. Each ln(tAs) doubling of tAs was associated with a 0.55-kg/m3 lower  ponderal index for infants of overweight/obese, but not normal-weight, mothers (pinteraction < 0.01). Finally, there was a significant interaction between maternal weight status, infant sex, and arsenic exposure on birth weight (pinteraction = 0.03). In girls born of overweight/obese mothers, each doubling of tAs was associated with a 62.9-g decrease  in birth weight, though the association was null in the other strata.Low-level arsenic exposure may affect fetal growth, and the associations may be modified by maternal weight status and infant sex.Gilbert-Diamond D, Emond JA, Baker ER, Korrick SA, Karagas MR. 2016. Relation between in utero arsenic exposure and birth outcomes in a cohort of mothers and their newborns from New Hampshire. Environ Health Perspect 124:1299–1307; http://dx.doi.org/10.1289/ehp.1510065 Fetal development is a period marked by rapid cell division, differentiation, and tissue growth and is therefore highly sensitive to the negative effects of toxic exposures. Arsenic is a naturally occurring chemical element found in bedrock and soil and it readily dissolves into surrounding groundwater that may be used for drinking . There iV), which is then reduced to monomethylarsonous acid (MMAIII) (III is converted to dimethylarsinic acid (DMAV), which is then reduced to dimethylarsinous acid (DMAIII). This biotransformation of iAs is incomplete and iAs, MMAV, and DMAV are all excreted in the urine  is methylated into monomethylarsonic acid (MMA(MMAIII) . In a sehe urine . Althougs of iAs , they has of iAs . MMAV anthan iAs ; howeverthan iAs .2). Given the importance of maternal weight status in fetal growth . Participants provided informed consent, and all study procedures were approved by the Internal Review Board at Dartmouth College.The New Hampshire Birth Cohort Study (NHBCS) is a prospective study designed to examine the associations of arsenic exposure on fetal growth and development during early childhood . In the 3, and age- and sex-adjusted birth lengths and birth weight percentiles were computed per the methods of Fenton , and sex at birth were collected from a medical record review by trained study staff. Ponderal index at birth was computed as kg/mf Fenton . Age- anf Fenton .III and AsV), MMAV, and DMAV (hereafter MMA and DMA) using a high-performance liquid chromatography inductively coupled plasma mass spectrometry system –transformed for analysis. In addition, we divided MMAV by iAs and DMAV by MMAV to calculate the primary and secondary methylation indices (PMI and SMI), respectively . Prepregnancy weight status was categorized as normal weight (18.5–25 kg/m2), or overweight or obese (≥ 25 kg/m2). Twenty-five women who were classified as underweight (< 18.5 kg/m2) before the current pregnancy were excluded from this analysis.Women were asked to report their usual weight when not pregnant as part of the enrollment questionnaire. Those values were combined with height abstracted from the medical records to compute prepregnancy BMI  between 24 and 28 weeks gestation, where blood was sampled 1 hr after ingestion of 50 g of glucose. For those with a 1-hr GCT result of 140–199 mg/dL an additional, fasting oral glucose tolerance test (OGTT) was performed, where blood was sampled hourly for 3 hr after ingesting 100 g of glucose. Because women with a 1-hr GCT result ≥ 200 mg/dL were considered to have had gestational diabetes, they did not undergo a subsequent OGTT. For this analysis, results from both the GCT and OGTT were used to define maternal gestational hyperglycemia. Specifically, gestational diabetes mellitus (GDM) was defined as a 1-hr GCT result ≥ 200 mg/dL or failing the 1-hr GCT (140–199 mg/dL) with two or more high values on the OGTT based on the American Diabetes Association criteria for a normal OGTT  of bloodp < 0.05 was used to define associations as statistically significant. Baseline characteristics, maternal urinary arsenic concentrations, infant gestational age, and infant birth size measures were summarized overall and compared by maternal prepregnancy weight status using chi-square test for categorical measures, t-tests for normally distributed continuous measures, and Wilcoxon rank-sum tests for non-normally distributed continuous measures.All analyses were completed using R: A Language and Environment for Statistical Computing, version 3.0.2 . A thresWe assessed the linearity of each dose–response relationship between each arsenic exposure (ln-transformed) and each outcome using generalized additive models (GAMs). GAMs were fit using the R language gam function included in the mgcv package. In our analyses, the shape of dose–repose relationship was estimated using thin plate splines . The quap < 0.10 level in bivariate analyses were included as covariates. In this study, we explored arsenic’s association with birth anthropometric outcomes while controlling for gestational age, and did not explore potential mediation of any associations by gestational age. The models for the head circumference, birth length, birth weight, and ponderal index outcomes were adjusted for maternal age at enrollment (continuous), maternal education  as a covariate.Models were fit in the overall sample and stratified by maternal prepregnancy weight status and infant sex. In all models, variables associated with maternal prepregnancy weight status or birth size at the Models with multiplicative interaction terms between the urinary As exposure and maternal prepregnancy weight status were used to formally assess modification by maternal prepregnancy weight status; a Wald test was used to determine statistical significance of the multiplicative terms. In addition, because other studies report potential sex-specific effects of arsenic exposure , we conda) excluding women who reported any seafood intake within the 2 days before the urine sample collection, b) excluding any women who reported any rice intake within the 2 days before the urine sample collection, and c) adjusted for creatinine for the subset of women with urinary creatinine concentrations available. We also repeated the analysis with the head circumference outcome additionally adjusting for a) vaginal or cesarean birth and b) birth weight. Finally, because prepregnancy weight status is positively associated with risk of developing GDM  and tAs was most strongly correlated with DMA . Unadjusted concentrations of maternal urinary arsenic did not differ by prepregnancy weight status, nor did the PMI and SMI.Among the 706 included participants, 300 (42.5%) were overweight or obese before their current pregnancy, and specifically 113 (16.0%) were obese. p = 0.43). Overweight or obese status was also associated with a larger head circumference at birth. When considering age- and sex-adjusted size at birth (data not shown), the average (SD) birth length percentile was 57 (29) among all infants and did not differ by maternal weight status (p = 0.12). The average (SD) birth weight percentile was significantly higher for infants whose mothers were overweight or obese compared to normal weight prepregnancy . Additionally, mothers who were overweight or obese gave birth to children with a 0.5 kg/m3 higher ponderal index, on average, though this difference was only marginally statistically significant (p = 0.07).Infant birth characteristics by maternal prepregnancy weight status are presented in p = 0.03] in head circumference. The association was not modified by maternal weight status and did not change after further adjustment for delivery mode and/or birth weight (see Table S1). Results were also consistent in unadjusted models (data not shown). The negative associations between maternal urinary iAs and DMA and head circumference were similar in magnitude to those for MMA and tAs, though they did not reach statistical significance.Results from the adjusted generalized additive models supported linear dose–response relationships between maternal urinary arsenic concentrations and gestational age, head circumference, birth length, birth weight, ponderal index, birth length percentile, and birth weight percentile (data not shown). p = 0.04) in birth length. Although the estimates of association were larger and only statistically significant in the overweight/obese stratum, the statistical test for interaction by maternal weight status did not reach statistical significance. The results for birth length percentile were qualitatively similar to those for birth length (see Table S2); birth length percentile was positively associated with ln(DMA) and ln(tAs) in the stratum of overweight/obese women, though only the ln(DMA) association was statistically significant.Maternal urinary DMA and tAs were positively associated with infant birth length. After holding all other covariates constant, each doubling of tAs was associated with a 0.15-cm increase  ponderal index. Additionally among those women, decreases in mean ponderal index also were observed with increases in urinary iAs, MMA, and DMA. In contrast, there were not any statistically significant associations between maternal arsenic concentrations and infant ponderal index among normal weight women.Maternal urinary arsenic was inversely associated with infant adiposity measured as ponderal index, and the associations appeared limited to women who were overweight or obese prepregnancy. Among children of those women, each doubling of tAs was associated with a 0.55-kg/mMaternal urinary arsenic was not significantly associated with gestational age, absolute birth weight , or birtp for interaction for ln(tAs) = 0.04]; among male infants, each doubling of tAs was associated with a 0.28-cm increase  in birth length. Results were similar for DMA and MMA. There was also a suggestive interaction between arsenic exposure and infant sex on the birth weight outcome; arsenic appeared negatively associated with birth weight in girls and positively associated with birth weight in boys, though none of the point estimates were statistically significant, nor were any of the interaction tests.In analyses that examined effect modification by infant sex (see Table S3), arsenic exposure was positively and significantly related to birth length in male infants but showed no association in females [p = 0.13). In the stratum of boys born of overweight/obese mothers, each doubling of tAs was associated with a 0.51-cm increase  in birth length. When looking at the birth weight outcome, there was a strong, statistically significant negative association between arsenic exposure and birth weight in the stratum of girls born of overweight/obese mothers, whereas associations in the other strata were largely positive and not statistically significant; the test for three-way interaction was significant (p = 0.03). In the stratum of girls born of overweight/obese mothers, each doubling of tAs was associated with a 62.9-g decrease  in birth weight.In analyses stratified by both maternal weight status and infant sex , the posn = 594) or rice (n = 543) in the previous 2 days, with additional adjustment for creatinine , or with additional adjustment for gestational weight gain . Also, we did not find evidence that the results of the maternal glucose challenge test or maternal hyperglycemia status modified the association between maternal urinary tAs and infant ponderal index (data not shown).Results were consistent in analyses limited to the subset of women who did not consume seafood  lower newborn head circumference after adjustment for maternal BMI and socioeconomic status. An analysis of ultrasound measurements from the Bangladeshi cohort  found that arsenic may influence head circumference before the third trimester and in a sex-specific manner  difference in birth weight when examining infants born of mothers with urinary arsenic concentrations < 100 μg/L. In contrast, we only observed a negative association between arsenic and birth weight in girls born of overweight/obese mothers.In contrast to our results, Three other prospective studies have examined maternal urinary arsenic in relation to birth outcomes . A studyThe results are also mixed from other studies that relied on maternal hair , maternaSeveral population characteristics could lead to the heterogeneity of study findings. Our research differs from the bulk of previous research on arsenic and fetal growth because of the relatively low levels of arsenic exposure in our population; for example, participants in the study by Fetal growth is a complicated process that is affected by several factors including maternal nutrition, genetics, and toxic exposures . Althougin utero arsenic exposure on fetal length at lower and higher exposure doses is plausible given the findings of an in vitro study of arsenic trioxide (ATO) and the growth of osteoblasts—the specialized cells in the bone that secrete collagen and a calcium- and phosphate-containing mineral to form the bone matrix  PDFClick here for additional data file.


Objective. To compare birth weight ratio and birth weight percentile to express infant weight when assessing pregnancy outcome. Study Design. We performed a national cohort study. Birth weight ratio was calculated as the observed birth weight divided by the median birth weight for gestational age. The discriminative ability of birth weight ratio and birth weight percentile to identify infants at risk of perinatal death  or adverse pregnancy outcome  was compared using the area under the curve. Outcomes were expressed stratified by gestational age at delivery separate for birth weight ratio and birth weight percentile. Results. We studied 1,299,244 pregnant women, with an overall perinatal death rate of 0.62%. Birth weight ratio and birth weight percentile have equivalent overall discriminative performance for perinatal death and adverse perinatal outcome. In late preterm infants (33+0–36+6 weeks), birth weight ratio has better discriminative ability than birth weight percentile for perinatal death  or adverse pregnancy outcome . Conclusion. Birth weight ratio is a potentially valuable instrument to identify infants at risk of perinatal death and adverse pregnancy outcome and provides several advantages for use in research and clinical practice. Moreover, it allows comparison of groups with different average birth weights. Gestational age at delivery and birth weight are considered important predictors of adverse pregnancy outcome , 2. AccuFetal growth is usually expressed in percentiles. Birth weight percentile curves are calculated from cross-sectional data of newborns . Thus, bBirth weight ratio (BWratio) is an alternative method to express growth of an individual with respect to the median. It is defined as the ratio of observed birth weight divided by the median birth weight of the population-specific reference growth curve. Values above 1 indicate “larger for gestational age than the median” and values below 1 indicate “smaller for gestational age than the median.” It may offer a solution to the limitations associated with BWpercentiles.Our objective was to compare BWratio and BWpercentile to express infant growth when assessing pregnancy outcome.This study was performed using a nationwide cohort using data from The Netherlands Perinatal Registry (PRN). The PRN consists of population-based data on pregnancies, deliveries, neonatal characteristics, and readmissions until 28 days after birth. The PRN database is obtained by a validated linkage of three different registries, the midwifery registry, the obstetrics registry, and the neonatology registry of hospital admissions of newborn neonates , 7. RecoThe data in the perinatal registry are anonymous; therefore ethical approval was not needed. The Dutch Perinatal Registry gave their approval to use their data for this study .+0 and 42+6 weeks gestation in The Netherlands between January 1, 1999, and December 31, 2007. All cases with congenital anomalies were excluded ∗100 [We also calculated—separate for BWratio and BWpercentile and four strata of gestational age at birth—the population-attributable risk (PAR) of abnormal fetal growth for perinatal death and adverse pregnancy outcome. PAR was based on the prevalence ( 1)]∗100 .Finally, receiver operator characteristics (ROC) curves were constructed for the whole cohort and for abnormally grown infants only, to compare discriminative ability of birth weight ratio and birth weight percentile for our outcome measures  in the four gestational categories. All statistical tests were 2-sided; a probability value of 0.05 was chosen as the threshold for statistical significance.The data were analyzed with the SAS statistical software package .n = 258,908 (15.82%)), multiple pregnancies ), infants with congenital anomalies ), and infants born before 25+0 weeks or after 42+6 weeks GA ). After application of the inclusion and exclusion criteria the study population consisted of 1,299,244 pregnancies. Baseline characteristics of the population are shown in From January 1, 1999 until December 31, 2007 a total of 1,636,565 pregnancies were registered in the PRN database. We excluded cases that were nonwhite  and term (37+0–42+6 weeks) infants is normally distributed. Distribution of the birth weight ratio of extremely premature (25+0–28+6 weeks' gestation) or very premature infants (29+0–32+6 weeks gestation) is negatively skewed.P < 0.001) than in the term group, 17.04%  (33–36 weeks), 25.73%  (29–32 weeks), and 37.8%  (25–28 weeks), respectively.The lines in P < 0.001) than in the term group, 8.43%  (33–36 weeks), 18.39%  (29–32 weeks) and 16.48%  (25–28 weeks), respectively.At term (37–42 weeks GA), 6.64%  of infants are born with a high BWratio (>1.25). In the preterm period, the incidences are significantly higher  –15.Incidences of perinatal death and adverse pregnancy outcome are shown in Figures Comparison of mortality rates in Figures On the other side of the growth spectrum, birth weight ratio also allows more precise differentiation between different severities of LGA.Both birth weight ratio and birth weight percentile show a gestation related death rate in the normal range  with higher death rates towards both ends of the growth spectrum. The same effects at the ends of the growth spectrum were found for adverse pregnancy outcome Figures .The percentage of perinatal death and adverse pregnancy outcome that can be attributed to abnormal growth depends on gestational age at delivery, on whether abnormal growth is defined by BWratio (low/high BWratio) or BWpercentile (SGA/LGA), and on the cut-off value that is used. PAR of abnormal fetal growth for perinatal death at different gestational ages is shown in The population-attributive risk of abnormal growth is higher for death than for adverse pregnancy outcome, which means that a larger percentage of perinatal deaths than adverse pregnancy outcome can be attributed to abnormal growth. PAR of suboptimal growth for perinatal death is small in extremely premature infants, increases with advancing gestational age with a peak in late preterm infants (33–36 weeks), and decrease at term.Also, PAR of abnormal growth, for example, for perinatal death at term, is higher if less stringent cut-off values to define abnormal growth are chosen .The areas under the receiver operator characteristics curves are shown in +0–36+6 weeks)  and at term  (+0–36+6 weeks) .When we assessed SGA cases only (birth weight below the 10th percentile), the discriminative ability of BWratio was better than that of BWpercentile for death in the late preterm period (33 P 0.05) . The disDiscriminative ability of BWratio for perinatal death or adverse pregnancy outcome is comparable to that of birth weight percentile. Birth weight ratio is—for smaller and larger than average infants—a more discriminative instrument for perinatal death and adverse pregnancy outcome than birth weight percentile.Our findings confirm an association between abnormal fetal growth and premature delivery –15, and Some limitations need to be addressed. First, a possible limitation is related to the use population-based birth weight percentiles. Although there exists no unanimity about the question whether references should be based on population birth weight characteristics or on individual growth potential, the latter might have better discriminative ability for adverse outcome , 17–23, A second potential limitation is the use of preterm birth weight as standards for preterm BWratio. This might have led to an underestimation of the effect of prematurity on pregnancy outcome because—as this study shows—prematurity is associated with abnormal growth.Finally, the PRN database does not contain data on how pregnancy dating is performed. Until 2011, no uniform pregnancy dating was performed in The Netherlands. Historically, it was common practice to date pregnancies based on LMP. Since the 1980's the use of ultrasound was gradually introduced in obstetric care. During our study period crown rump length and head circumference measurements had already increasingly replaced LMP for dating, but no quantitative data are available on how pregnancy was dated in individual cases. Inaccurate pregnancy dating might be partially responsible for the wider birth weight spread in the preterm period. However, this wider spread in the preterm period might also be the result of higher incidences of pathologically small or large growth, leading to preterm delivery.The main strength of this study is the size  and composition  of the cohort. The incidence of fetal deaths, neonatal deaths, perinatal deaths, and composite morbidity that we found in this study is in accordance with previous research [Data are derived from a large, well-maintained population-based national perinatal registry (1999–2007). The vast majority of the caregivers contribute to the PRN registry; therefore, it comprises approximately 96% of all pregnancy and birth characteristics in The Netherlands. The 4% missing birth data are due to 1-2% nonreporting general practitioners and 2-3% nonreporting midwives. The use of population-based growth curves for white mothers, separate for gender and parity, minimizes risk of systematic bias caused by one of these factors.Finally, this is to our knowledge the first study that compared birth weight ratio and birth weight percentiles using ROC curves. This allowed us to statistically substantiate our findings.As shown in This study confirms that preterm delivery is associated with increased SGA and LGA rates as compared to term delivery , 15 and +0–32+6 weeks)  . PercentFinally, birth weight ratio allows comparison of groups with different average weights and weight distributions, for example, male infants and female infants or infants of different ethnic origins. Percentiles and ratios both allow comparison of groups, but—as explained before—information on distance from the mean and the incidence of different ratios within a population is lost when percentiles are used.The different representation of growth with BWratio and BWpercentile that is explained above has two effects that result in better interpretability of infant growth when BWratio is used.First, the use of BWpercentile causes a loss of discriminative power, especially among SGA and LGA infants. For example, all infants with birthweight <1st percentile (1% of the population and BWpercentile distribution) cover about 10% of the BWratio distribution, thus allowing better differentiation within this group of small infants with BWratio.Second, birth weight percentiles suggest that an infant (born at term) with a birth weight at the 25th percentile is much lighter than an infant at the 75th percentile. However, this is not the case. The birth weight ratios in this example are 0.9 (25th percentile) and 1.1 (75th percentile) and are both very close to 1.0. The seemingly large difference if growth is expressed in percentiles is caused by the fact that a population is by definition divided into 100 equally large groups instead of groups based on birth weight in relation to the median and that most infants have a birth weight close to the median.There are two reasons for the fact that neither BWpercentile nor BWratio has high sensitivity and specificity for death and adverse outcome and that only a limited proportion of death and adverse outcome can be attributed to fetal growth below the 10th percentile.First, unlike other tests, increased risk of death (or adverse outcome) is not associated with a one-directional change in the risk factor (birth weight ratio or percentile). Both low and high birth weight ratios (or percentiles) are associated with increased risk of death (or adverse outcome).Second, both death and adverse outcome occur at all gestational ages and across the whole growth spectrum. There seems to be a gestational age related basic risk  with increased death and adverse outcome rates at both ends of the growth spectrum.In view of the results we think that BWratio could complement BWpercentiles in clinical practice and can play a role in scientific research. It allows differentiation of SGA infants that is not possible with BWpercentile; it is easy to understand and therefore useful for patient counseling. This study provides in our opinion sufficient evidence for clinicians to use birth weight ratios when assessing risks of adverse outcome when an infant is suspected to be extremely small or large for gestation. Finally, birth weight ratio enables comparison of populations with different baseline characteristics.This study shows the need to redefine cut-off values that define abnormal fetal growth. Historically, these cut-off values have been set at the 2.5th, 5th, and 10th percentile. However, this study shows that the relation between fetal growth and the risk of adverse pregnancy outcome differs depending on gestational age at delivery. Therefore, future research should focus on defining cut-off values to identify infants at risk of clinically relevant poor growth. To do this, consequences of abnormal growth should be weighed against potential treatment benefit of early detection and intervention, also taking into account costs of follow-up and potential adverse effects of interventions. Given the potentially better associations with adverse pregnancy outcome, such research should be performed using customized weight percentiles.


More than 1 in 10 infants are born prematurely worldwide, making preterm birth the leading cause of neonatal mortality and morbidity. Chronic maternal stress is increasingly recognized as one of the contributing risk factors for preterm birth, yet its specific role remains largely unknown. Examining the exposure to stressors over a mother’s life course might provide more perspective on the role of maternal stress in preterm birth. Our aim was therefore to retrospectively explore the associations between chronic, lifelong stressors and protective factors and spontaneous preterm birth.This study was part of a large case–control study based in Edmonton, Canada, examining gene-environment interactions and preterm birth. Cases were mothers with a spontaneous singleton preterm birth (<37 weeks) without preterm premature rupture of membranes. Controls were mothers with an uncomplicated singleton term birth without a history of preterm birth. Sociodemographic and medical data were collected. A postpartum telephone questionnaire was administered to assess stressors across the lifespan. Both individual and contextual variables that could influence stress response systems were examined. Overall, 622 women were included, of which 223 subjects – 75 cases and 148 controls – completed the stress questionnaire. Univariate and multivariate logistic regression analyses were performed.P = 0.024). The adjusted odds ratio for the ACE score was 1.18 , suggesting that for every increase in childhood adverse event endorsed, the risk of preterm birth increased by 18 %. Lifetime physical and emotional abuse was also associated with spontaneous preterm birth in our study population .Multivariate analysis showed that exposure to two or more adverse childhood experiences (ACEs) was associated with a two-fold risk of preterm birth, regardless of maternal age, smoking status, educational status, and history of miscarriage  contains supplementary material, which is available to authorized users. With an estimated 15 million preterm births annually worldwide, the global burden of preterm birth (defined as birth before 37 completed weeks of gestation) is enormous [Significant antepartum and postpartum maternal stress can arise from multiple current circumstances or past sources across the lifespan; however, its specific contribution to the risk of preterm birth remains controversial. High levels of psychosocial stress experienced by women before or during pregnancy increase the risk of preterm labour –8. WomenLow socio-economic status is also believed to be an important risk factor for preterm birth –20. SociMaternal stress and preterm birth share the elements that they are complex entities with many different environmental and psychosocial components. Joint examination of common stressors and individual socio-economic, psychosocial, and behavioural risk factors should therefore provide an effective strategy for increasing our understanding of the complex causes of preterm delivery –30. In tChronic stress can lead to dysregulation of the neuroendocrine system resulting in acceleration of disease processes, an increase in inflammatory cells and cytokines and chronic activation of the inflammatory response –33. HencThe objective of this study, therefore, was to retrospectively explore the associations between chronic, lifelong stressors and spontaneous preterm birth in our case–control study. We hypothesized that stress scores would be higher in women with preterm birth compared to controls, suggesting that higher levels of chronic maternal stress increase the risk of spontaneous preterm birth.This study was part of a case–control study based in Edmonton, Canada, examining both genetic variants and environmental factors in the etiology of spontaneous preterm birth. Cases were defined as mothers who gave birth to a singleton at less than 37 weeks gestation (preterm) as a result of spontaneous idiopathic preterm labour with intact membranes. Both vaginal delivery and caesarean sections were included as cases as long as the women with uncomplicated preterm caesarean sections began contracting spontaneously prior to 37 weeks. Controls were mothers with either a spontaneous, uncomplicated birth of a singleton at 38 to 41 weeks gestation or an (elective) uncomplicated caesarean section between 38 and 41 weeks gestation. Women with a delivery between 37 and 38 weeks were excluded to clearly delineate the groups due to the accepted error in dating gestation by last menstrual period or second trimester ultrasound (±7 days). Similarly, women with a history of preterm delivery in the control group were excluded. Other exclusion criteria included pre-eclampsia, placental abruption, uterine malformations, minor and major fetal malformations, HIV or AIDS, influenza, H1N1, and non-English speaking women. The Human Research Ethical Boards of the University of Alberta, Alberta Health Services and Covenant Health, approved the study. Between January 2009 and August 2010, women who delivered in one of the three Edmonton hospitals were approached and screened for eligibility. Written informed consent was obtained from all subjects before participating in the study.Demographic and medical data were primarily collected from medical charts. It was not possible to obtain all desired data as medical records were often incomplete or certain demographic data were not recorded. Missing data were collected via self-report at enrolment and/or during follow-up telephone interviews.Maternal medical charts were used to extract key medical variables and risk factors for preterm birth. Maternal data included maternal age, parity, height, and pre-pregnant weight for body mass index calculation, smoking, alcohol, and drug use. A history of uterine malformations, cervical procedures, medication use and pre-existing medical conditions, such as hypertension, diabetes mellitus, and autoimmune diseases, were also recorded. Additionally, information regarding mode of conception, method of gestational age determination, blood pressure (both early gestation and at term), cervical cerclage, and any medication use during pregnancy. We also extracted data concerning common complications during pregnancy, including genital tract infections and sexually transmitted infections, gestational hypertension or diabetes, polyhydramnios, and placental complications. Labour and delivery records were examined to determine gestational age at delivery, type of labour and mode of delivery, medication, evidence of maternal infection, and placental histopathology. Fetal data abstracted from the delivery records included sex of the infant, Apgar scores, cord pH, congenital malformations, and the evidence of infection in the first 48 hours.Self-reported variables included height and pre-pregnancy weight, ethnicity, determinants of socio-economic status , substance use, self-report medical and obstetric history, previous preterm births and/or miscarriages, and family history of preterm birth. Ethnicity was reported by self-identification back three generations from both the maternal and paternal side, where possible.Data were subsequently stored in the online database and downloaded onto spreadsheets for analysis.For the assessment of chronic, lifelong stressors, we designed the ‘Well-being and Pregnancy Questionnaire.’ Using this questionnaire, both individual and contextual variables that influence the stress response were examined for all subjects. It incorporated several checklists designed for this study and validated research instruments to measure concepts related to stress and personal resources. They included perceived stress, common stressors during pregnancy, social support, life events, coping, adverse childhood experiences (ACEs), adult interpersonal violence experiences and depression. Instruments were chosen after review of the literature and based on their possible direct and/or indirect association with spontaneous preterm birth. Where possible, we used validated tools available in the public domain. In short, the questionnaire is comprised of the following tools: Perceived stress, Common stressors in pregnancy, Interpersonal Support Evaluation List (ISEL) , Life EvBetween 3 months and 1 year postpartum, participating subjects were contacted by telephone where possible for follow-up and administration of the ‘Wellbeing and Pregnancy Questionnaire.’ To maximise the number of respondents, we attempted to contact each participant at least three times at different times during days and evenings. Answers were entered into our secure online database.2 or binominal logistic regression, and odds ratios (OR) and 95 % confidence intervals (95 % CI) were recorded. A P value <0.05 was considered significant.All data were analyzed using SPSS 19.0 statistical software. Before analysis, data from different sources were merged and the data set was cleaned. Data were coded or recoded for analysis when required and missing data were indicated. Demographic and medical variables were compared between case and control subjects. For this univariate analysis, variables were compared using χP value <0.05 was considered significant. Finally, multivariate logistic regression was performed. A multivariate model was created including the demographic variables that were significantly different between cases and controls in our population. Adjusted ORs and 95 % CIs were reported.Scores for all questionnaire tools were separately calculated using predefined scoring keys. We also calculated a combined childhood and adult abuse score. For this score, the separate scores of childhood abuse, childhood neglect and adult physical and emotional abuse were added. In addition, a total combined stress score was computed, so that tools that represent stressors were added, while tools that represent modifiers of the stress response – social support and adaptive coping – were subtracted. This score was computed so that all stressors had the same weight. Univariate analysis was performed on all separate questionnaire tools and the total stress score to assess the relationship with spontaneous preterm birth. Some scores were also dichotomised based on their median split and subsequently analysed. Variables were compared using binominal logistic regression and ORs and 95 % CIs were recorded. A A total of 622 women were recruited to the study; 210 case group participants and 412 control group participants. In total, 234 telephone questionnaires were administered . However, 11 study subjects that completed the questionnaire were later excluded from the study following secondary exclusion. Reasons for exclusion were uterine malformation (1 respondent), delivery between 37 and 38 gestational weeks (4 respondents), preterm premature rupture of membranes (1 respondent), placental abruption (1 respondent), history of preterm birth in control (1 respondent), and no spontaneous preterm labour (3 respondents). As a result, 223 completed telephone questionnaires were included in the study as responded by 148 controls and 75 cases. Our final call rate was 36 % for controls (148/412) and 36 % for cases (75/210).All socio-demographic and medical variables were compared between the case and control group (210 vs. 412 women) and their possible relationship with spontaneous preterm birth was assessed. Table P <0.001). On a continuous scale, maternal age was significantly inversely associated with spontaneous preterm birth . Overall, mothers in our case group were younger than controls (mean age 28.3 years vs. 29.6 years). Not surprisingly, substance use was also associated with spontaneous preterm birth. The ORs of smoking, alcohol use, and street drug use were 2.08 (1.41–3.09), 3.51 (1.36–9.04), and 3.89 (1.12–9.32), respectively. In addition, educational status had a significant relationship with preterm birth in our population. Of the women in the control group, 75 % completed education beyond high school, whereas only 55 % of the women in the case group completed undergraduate education. Other factors of socio-economic status, such as marital status and income, were not different between cases and controls, nor was ethnicity. Notably, a history of one or more miscarriages in previous pregnancies was significantly associated with preterm birth . None of the medical variables were significantly associated with spontaneous preterm birth in our study population.Gestational age and birth weight were significantly different between cases and controls . We found a significant relationship between the computed Total Stress score and spontaneous preterm birth after univariate logistic regression, showing a crude OR for the risk of preterm birth of 1.46 . After dichotomization, a high stress score had an even greater crude OR of 1.86 . The presence of depressive symptoms during pregnancy was significantly associated with preterm birth in our univariate analysis . A lifetime history of major depression had a fairly high crude OR of 1.70; however, this was not significant .2 test for trend confirmed there was a linear trend (P = 0.003).When examining more specifically the relationship between ACE score and spontaneous preterm birth, we found that the proportion of women with preterm birth gradually increased with increasing number of ACEs. Inversely, the percentage of women with a term birth decreased as the number of ACEs increased Figure . The χ2 Univariate analyses of the socio-demographic and medical variables demonstrated that maternal age, smoking, alcohol and drug use, educational status, and a history of miscarriage were significantly associated with preterm birth in our study population. In our multivariate model, we therefore included maternal age, smoking, education, and history of miscarriage as covariates. We excluded alcohol use and drug use since the number of respondents reporting alcohol and/or drug use was very small.The ACE score was almost significantly associated with spontaneous preterm birth after adjusting for maternal age, smoking, and educational status Table . The adjThis study demonstrates that there is a strong relationship between ACEs – assessed with the ACE survey – and spontaneous preterm birth in later life. Every additional ACE increased the risk of spontaneous preterm birth by 18 %. This was after adjustment for maternal age, smoking, educational status, and history of miscarriage, all of which were found to be confounding factors in our study. More importantly, being exposed to two or more ACEs prior to the age of 18 was associated with a highly significant two-fold increase in the risk of delivering an infant preterm. Given the baseline risk of spontaneous preterm birth of around 9 % to 10 % [We also found that ACEs were more prevalent in cases than controls (data not shown). For instance, 18 % of the women in our case group experienced sexual abuse as a child compared to 8 % of the control women. A similar difference was seen in the prevalence of emotional neglect: 20 % and 6 % of the women were emotionally neglected during childhood in the case and control groups, respectively. Apart from criminal behaviour, i.e., a household member imprisoned, all ACEs regarding household dysfunction were very common in both groups of women, occurring in up to 23 % of cases. In addition, almost a quarter of all women with a preterm birth admitted to being physically abused during childhood, while 15 % of the controls experienced physical abuse as a child.ACEs have been found to be associated with a large diversity of long-term negative health outcomes and risky behaviour , includiEvidence suggests that ACEs can lead to hyper-reactivity of the hypothalamic-pituitary-adrenal and sympatho-adrenal-medullary axes in response to stress in adulthood . This efOur telephone survey response rate was surprisingly high, at 37.6 %, given response rates for telephone surveys have been steadily declining over two decades . NeverthA concern for every retrospective study is the issue of recall bias. Fortunately, research indicates that the number of non-responders has little or no effect on the validity of the data obtained from the responders –60. RegaWe decided that, for our purpose, the preferred method of stress assessment was via telephone as there were no convenient or routine clinic appointment times and the response rate from a mailed questionnaire would be too low. In addition, some of the questions in the questionnaire might be upsetting and require direct contact with the study coordinator. The time for the telephone questionnaire was kept to less than 30 minutes; this limited the number of instruments that could be used. Our telephone response rate was 37.6 %, which is well above average for such telephone questionnaires. There were no significant differences in the characteristics of the populations who responded to the telephone interview from the characteristics of the overall group recruited.Our population was mostly Caucasian  with an insignificant contribution from other races. There were no differences in ethnic background  between the case and control groups. Hence, the association between ACEs and preterm birth observed was not due to race. After adjusting for confounding variables, a high total stress score – comprising all measures of chronic stress – was not associated with spontaneous preterm birth. One explanation for this could be the small sample size, but it could also be explained by the method of calculating this score. No composite measure of chronic stress, including perceived stress, common stressors, social support, life events, coping, ACEs, adult abuse, and depression exists in the literature nor does any standardized calculation of chronic stress. The development of better and, most importantly, standardized composite measures of chronic stress will aid in the assessment of chronic stress and the overall role of allostatic load in preterm delivery.In summary, ACEs are associated with spontaneous preterm birth. After adjustment for confounding variables, we found that women who were exposed to two or more ACEs have a notable two-fold increase in the risk of preterm birth. In addition, lifetime abuse was also linked to preterm birth. The data demonstrate that stressors throughout life can have a significant effect on pregnancy outcomes, including preterm birth. A healthy pregnancy therefore starts long before conception.


Birth weight centile curves are commonly used as a screening tool and to assess the position of a newborn on a given reference distribution. Birth weight of twins are known to be less than those of comparable singletons and twin-specific birth weight centile curves are recommended for use. In this study, we aim to construct gestational age specific birth weight centile curves for twins born in south India.The study was conducted at the Christian Medical College, Vellore, south India. The birth records of all consecutive pregnancies resulting in twin births between 1991 and 2005 were reviewed. Only live twin births between 24 and 42 weeks of gestation were included. Birth weight centiles for gestational age were obtained using the methodology of generalized additive models for location, scale and shape (GAMLSS). Centiles curves were obtained separately for monochorionic and dichorionic twins.Of 1530 twin pregnancies delivered during the study period (1991–2005), 1304 were included in the analysis. The median gestational age at birth was 36 weeks . Smoothed percentile curves for birth weight by gestational age increased progressively till 38 weeks and levels off thereafter. Compared with dichorionic twins, monochorionic twins had lower birth weight for gestational age from after 27 weeks.We provide centile values of birth weight at 24 to 42 completed weeks of gestation for twins born in south India. These charts could be used both in routine clinical assessments and epidemiological studies. Rate of twin births is rising due to the increased use of assisted reproductive technologies in the recent years . Birth wTwin births are further complicated by placental chorionicity. Monochorionic twins present a two to three times higher risk for adverse outcomes than dichorionic twins , with biPast studies have indicated that placental chorionicity should be taken into consideration in assessment of twin births . The lacThis study was based on labour room records and medical records maintained by the Department of Obstetrics and Gynaecology at the Christian Medical College, Vellore. This hospital serves as a maternity centre with almost 9000 deliveries annually . It provides obstetric care to local population of Vellore city and for surrounding towns and villages and also acts as a tertiary hospital. Besides women from Vellore district, women from neighbouring districts in Tamil Nadu and adjoining states of Andhra Pradesh and Karnataka also deliver in the institution. Most pregnancies are registered early during the first or second trimesters and followed up thereafter for antenatal care.Ethical approval of the study protocol was obtained from the Institutional Review Board (IRB: 2000-no.4481) of the Christian Medical College, Vellore. However, because of the retrospective nature of the study and data were extracted from medical records/labour room registers with no individual identifications, and hence individual informed consent was not obtained.The study sample included all twin pregnancies delivered at the centre between January 1, 1991 and December 31, 2005. Pregnancies in which at least one child died, or missing information on study variables were excluded from further analysis.Birth weight was measured immediately after birth on a Braun weighing scale to the nearest 50 g. Gestational age was determined as the number of completed weeks of gestation from the last menstrual period (LMP) to the date of birth. This was best estimated using combinations of the last menstrual period (LMP), early clinical examination and early ultrasound scans. If there was a difference between gestational age estimated from LMP and ultrasound, the ultrasound estimate was used. Placental chorionicity was diagnosed by ultrasound and confirmed by gross examination of placenta after the birth.From an initial exploratory analysis, we found that the distribution of birth weight at extreme gestational ages was non-normal and the general pattern of relationship between birth weight and gestational age was not linear. Given these violations in the usual assumptions of regression analyses, we chose the generalized additive model for location scale and shape (GAMLSS) approach . This apDuring the study period, a total of 1673 multiple pregnancies were delivered. Of which, the following were removed sequentially from further analysis . Thus complete data were available from 1304 twin gestations for analyses. The mean maternal age of mothers included in the sample was 25.2 years (SD = 4.3) and 46 % of mothers were primigravid. A total of 88 (6.7 %) mothers were conceived using some form of assisted reproductive technologies, while approximately (64) 5 % of mothers experienced gestational diabetes and (224) 17 % had preeclampsia. Eighty two percent of the women in our sample were Hindus, 11 % were Muslims and 7 % were Christians. About 8 % (106) of mothers were illiterates. There were 457 (35 %) monochorionic pregnancies.The median gestational age at birth was 36 weeks (IQR 34–38 weeks). There was a 4.1 % increase in adjusted  mean birth weight from 2050 g in 1991 to 2135 in 2005. Dichorionic twins were heavier than monochorionic twins with an adjusted  mean of 2138 g compared with 2, 054 g respectively. The mean birth weight discordance was 13.1 % . Considering a threshold of 18 % [Examining the 50th centile, the weekly increase in birth weight flattens by 38 weeks of gestation and thereafter gain in the median birth weight was negligible Fig. . To asseFigure In this study, we constructed new birth weight centile curves for twins born in South India. We have presented centile curves by chorionic placentation to facilitate consideration of chorionicity in the assessment of twin births.The overall pattern of change in birthweight over gestational age was characterized by a rapid change in weight till 38 weeks and reduction in change then onwards. Given that it is increasingly possible to determine chorionicity prenatally, it is important to consider placental chorionicity in the assessment of growth in twins. Our comparison of centile curves by chorionicity showed that birth weights of monochorionic twins were lower than dichorionic twins in gestational ages between 28 and 42 weeks. This could be explained by the increased demands with advancing gestational age in monochorionic twins which share a common placenta and this heightened demand may not be met as adequately as in dichorionic twins- leading to the difference between two groups.Previous studies on distribution of birth weights in India have mainly been based on singleton births. Birth weights from our study were consistently lower than those of singletons ; the difThe data presented here is based on the largest sample size reported till date from India. However, in developing settings like India, it is considerably difficult to obtain precise obstetrical records on measurements at birth for a large number of twins, as there are not many population based twin registries. The new birth weight curves may provide useful evidence for better understanding the birth weight of twins born in South India. For instance, it could serve as a useful tool for clinicians to evaluate and assess the birth weight of newborn twins. Additionally, this new centile curves should be a useful for epidemiologic research on twins related to determination of geographic differences, temporal trends and etiologic determinants of distribution of birth weight.One of some limitations of this study is that the data were drawn from a tertiary care hospital, and hence it may restrict the generalizability of our results. However, given that twin pregnancies are considered as high-risk and often referred to tertiary care hospitals, the problems related to generalizability might be less likely. Another limitation is the measurement of gestational age using dates of last menstrual period, which suffers from recall bias. We believe our estimates are likely to be improved with the use of early ultrasound to correct estimates of gestational age. Also, data on birthweight and placental chorionicity captured during the course of routine clinical care may not be as precise as measurements under more controlled research settings. Thus, for example, we were not able to ascertain the extent of intra or inter observer variability. Additionally, the number of infants in extreme gestational ages was not sufficiently larger to enable accurate estimation of centiles. Further, in our study, the inclusion criteria resulted in a more general reference for birth weight, describing the variation in birth weight within a reference population and did not delineate variation that can be considered ‘ideal’ or of ‘desirable targets’. Despite these limitations, our study will add to the existing scanty literature on birth weight distributions for twin births and will provide basis for future epidemiological studies on twins from this region.The use of population specific birth weight centile curves will better aid both the clinician and researcher in the assessment of the birth weight of twins. Further, we recommend that assessment in twins consider placental chorionicity. The charts will provide a benchmark to examine the birth weight of twins in relation to other twins born of same gestational age, and would serve as a baseline for future epidemiological research studies. Future work will be to assess whether the infants identified in this way are those with high risk for poor perinatal outcomes, such as stillbirth and neonatal death.


The paper discusses the coupled attitude-orbit dynamics and control of an electric-sail-based spacecraft in a heliocentric transfer mission. The mathematical model characterizing the propulsive thrust is first described as a function of the orbital radius and the sail angle. Since the solar wind dynamic pressure acceleration is induced by the sail attitude, the orbital and attitude dynamics of electric sails are coupled, and are discussed together. Based on the coupled equations, the flight control is investigated, wherein the orbital control is studied in an optimal framework via a hybrid optimization method and the attitude controller is designed based on feedback linearization control. To verify the effectiveness of the proposed control strategy, a transfer problem from Earth to Mars is considered. The numerical results show that the proposed strategy can control the coupled system very well, and a small control torque can control both the attitude and orbit. The study in this paper will contribute to the theory study and application of electric sail. The electric solar wind sail, electric sail for short, is an innovative propulsion concept. Similar to the more conventional solar sail, it can produce continuous and propellant-less thrust. Unlike the solar sail, the electric sail is accelerated by the solar wind dynamic pressure, instead of the photon momentum. As seen in r)2, and that of the electric sail decays as 1/r, where r is Sun-spacecraft distance. Therefore, for a given characteristic acceleration, an electric sail can provide a greater thrust than a solar sail, if the solar distance is greater than 1 astronomical unit (au). An in-depth analysis of the electric sail performance can be found in T in the orbital frame.The propulsive thrust per unit length of tether is given in  asdFdl=obxbyb plane. Therefore, the unit direction vector of the kth tether in the body frame is [cos(2π(k-1)/N) sin(2π(k-1)/N) 0]T. Consequently, the unit direction vector of the kth tether in the orbital frame can be given byBecause of the existence of centrifugally stabilizing auxiliary tethers , it can r T. The control variables are thrust control coefficient κ and attitude angles ϕ, θ, which can influence the thrust vector. In the trajectory optimization for spacecraft, the objective function to be minimized usually is fuel consumption or transfer time. Since the electric sail can use the solar-wind-dynamic pressure to generate thrust without fuel consumption, the transfer time is always selected as the optimization performance index to be minimized .J=.J=r, Θ,t0, the initial state of the electric sail should be consistent with the orbit parameters of Earth and can be written asr⊕(t0), Θ⊕(t0), Ψ⊕(t0), vr⊕(t0), ωΘ⊕(t0) and ωΨ⊕(t0) are the orbital parameters of Earth.Assuming that the electric sail is in the Earth's orbit at the initial time tf, the final state of the electric sail should be consistent with the orbit parameters of target planet and can be written asr⊗(tf), Θ⊗(tf), Ψ⊗(tf), vr⊗(tf), ωΘ⊗(tf) and ωΨ⊗(tf) are the orbital parameters of the target planet which can be calculated from orbital elements.Assuming the electric sail arrived in the orbit of target planet at the final time For the orbital control problem of electric sails, the hybrid genetic algorithm Gauss pseudospectral optimization method is implemented in this paper. As seen in As seen in attitude dynamical equations Eq , there ix can be defined as x = [ϕ θ ψ ωbxωbyωbz ]T. Since the attitude angel ϕ and θ can influence the orbit, the output vector y is defined as y = [ϕ θ]T and the input vector u is defined as u = [TxTy]T. Considering the attitude dynamical equations Eq  and B(x) are defined as following,LgiLfhj (x) and hj(x) with respect to the vectors f(x) and gi(x), with j, i = 1 to 2. When θ is not equal to π / 2, the matrix Λ(x) is nonsingular. The state feedback can be applied to the nonlinear system,It has been obtained that nonlinear system has the vector relative degree {2 2}. Therefore, the feedback functions Λ(The nonlinear system (26) could be transformed to a linear one in the form of,ϕd and θd are the referenced attitude angles generated by orbital controller, and c1, c2, c3, c4 > 0 is attitude controller parameters.The system now consists of two second-order decomposed channels which can be controlled by two independent controllers. Based on the pole assignment method, the new control variables can be selected asa⊕ = 2mm/s2 corresponds to an estimated maximum value of the propelling acceleration at r = r⊕ that will probably be available in a near future [ϕd, θd and proportionality coefficient κ are generated by the hybrid optimization method. In the initial solution calculation via GA, the quantity of LG points is 30. The population size is 100, and the number of generations is 200. In the optimal solution calculation via SQP, the quantity of LG points is 90. The convergence criteria for the NLP are a maximum constraint violation of 1×10−9 (TolCon) and a reduction in the cost function of less than 1×10−6 (TolFun) for one iteration. In attitude control, controller parameters are selected as c1, c3 = 5×10−5 and c2, c4 = 2.5×10−10. The initial attitude angles are assumed to be equal to the referenced attitude angles. The detailed implementation can be seen in the simulation program Click here for additional data file.