C3 C4 Ratio

[Miss Ruhnke]

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Acute coronary syndrome is an inflammatory disease, during which the complement cascade is activated. We assessed the complement C3 and C4 concentration ratio (C3/C4 ratio) in serum as a potential measurement to predict cardiovascular attacks. Patients with acute coronary syndrome (n=148) were followed after an initial attack for subsequent ischemic cardiovascular events (composite end point of death, myocardial infarction, recurrent unstable angina, or stroke). During the follow-up period (average 555 days), 44 patients met an end point. Blood samples were taken at hospitalization, 1 week, 3 months, and 1 year after hospital admission. Serum complement C3 and C4 concentrations and the C3/C4 ratio were analyzed. Patients with an end point had, throughout the follow-up period, a higher C3/C4 ratio than patients without these end points (repeated measures analysis of variance, p=0.007). When all traditional cardiovascular risk factors and other potential confounding factors were included in a Cox multivariate logistic regression survival analysis, the C3/C4 ratio emerged as the novel risk factor for any new cardiovascular event (odds ratio 1.33, 95% confidence interval 1.08 to 1.63, p=0.007). When the C3/C4 ratio was divided into 4 quartiles, 24% in quartiles 1 and 2 (lowest) and 48% in quartile 4 (highest) had end points during follow-up (odds ratio 3.04, 95% confidence interval 1.27 to 7.29, p=0.01). In conclusion, increased serum C3/C4 ratio is a readily available and novel marker for recurrent cardiovascular events in acute coronary syndrome. The relative increase in serum C3 protein and decrease in C4 protein could explain changes in the C3/C4 ratio.

The authors have worked with a variety of samples viz. vegetation, soil, suspended particulate matter, riverbed sediments from the Godavari region which is commendable. But I am curious as to why the authors chose isotopic analyses for the study. The authors must note that there are other much stronger techniques that can be applied for palaeovegetation reconstruction, such as geochemical biomarkers or compound specific isotopic analyses of normal alkanes. These provide much more detailed/spot on information without significantly less biases or overlaps. So the authors must signify and explain very clearly the selling point of this paper and strength of the technique that has been used.

I would also like to add that unless a journal's terms and conditions require so, it is generally not a good idea to combine "results and discussion". Separating the two makes it much clearer as to what your own data and results are depicting and the discussion would include clear explanations of your results. It is important for readers to identify the original data of your research and separate them from previous literature data and knowledge that come under discussion part.

Page 6, Line 150-152: There is no mention in the introduction as to how microbial inputs or early diagenetic alterations and early decomposition of organic matter might affect the isotopic signatures. The present approach is highly one-dimensional primarily considering input of C3 vs. C4 vegetation in connection with wetter and drier conditions. Authors must take into account all the other factors, particularly those which significantly influence isotopic fractionation.

Page 15, Line 350-352: "Moreover, the Godavari C3 plants were not evenly distributed over the entire precipitation range. Together, this resulted in a relatively weak linear correlation with MAP for the individually measured C3 plants" - This contradicts the previous sentence on line 341. If the effect of MAP on isotopic values are significant, shouldn't the correlation be high?

Page 16, Line 384-386: "This isotopic contrast corresponds with the vegetation distribution in the basin, with mixed C3 and C4 vegetation in the upper basin and more C3 plants in the lower basin" - Is the vegetational input only controlling factor for the isotopic values? What about any signatures of soil bacteria?

Page 18, Line 408-410: "However, C4-derived OC has also been shown to be preferentially incorporated into fine fractions where it is better protected against degradation, whereas C3-derived OC is preferentially added to the coarse fraction thus leaving it less protected" - What governs this affinity for the C4 plants towards finer fractions whereas C3 plants towards coarser fractions?

The authors appear to have neglected the changing atmsopheric d13C over recent decades and how that would affect carbon in modern plants, and potentially older soils and fluvial SPM. Literature comparisons span 1970s to present and it needs accounting for. Please add discussion of (likely) age of materials and the Suess effect, throughout wherever relevant, and account for this numerically.

Godavari specific endmembers, this would be more generally interesting if we were told right away if this is the wet or dry end of C3 etc, unlikely that there are regional plant species effects, likely it is just the usual canopy etc effects.

d13C data points with the rainbow colors can be discerned by most readers using the legend, the coloring is not intuitive, for wet to dry try green to brown for example, and it would be better to pick a color scheme that can be seen by all readers.

b) Why are upper and lower basins parsed. Are these much different, probably not as the C4 distribution in lower basin falls within that for the upper basin, and the same for C3 with the upper basin just having a bit more range. Maybe overlay the two bar charts or use violins, to display the data if you want to keep with this 2 category, but if you do an T or F test do you find they are significantly different? (this panel is repeated in fig 3) fig. 2b can therefore be deleted.

I disclosed prior to accepting this review, that I collaborated with Kirkels and Peterse previously: Following my field and lab work in 2013-5 in the Andes, the GDGT aliquots went to Kirkels/Peterse, and their lab analyses for that project and collaborative discussion was done in 2015-2017 including conference poster and manuscript preparation, their manuscript was submitted in 2019 and the publication dated 2020 [3].

Measurements of serum complement components C3 and C4 are useful in the diagnosis and monitoring of immune complex disease e.g. SLE and some blood associated infectious diseases. Complement concentrations are acute phase proteins and may be normal, despite complement consumption, in some inflammatory and infective disorders.C3 and C4 are measured at the same time since this gives an indication of the complement pathway (classical or alternative) which is being activated and thus the cause of this activation. C3 alone is often decreased in infectious disease (septicaemia, endocarditis), C3 and C4 are often both decreased in immune complex disease, C4 alone is characteristically decreased in angioedema, immune complex diseases particularly vasculitis and in cryoglobulinaemia and cold agglutinin disease.

Measurement of serum complement is useful in the monitoring of specific immune complex diseases e.g SLE and infectious diseases post streptococcal disease, subacute bacterial endocarditis. Consumption of one or both components may also be useful prognostically e.g nephritis in lupus.

Though genetic deficiencies of C3 are exceedingly rare, deficiencies in other components which are more common (though still very rare) can result in low C3. Genetic deficiencies in C4 are rarely detected. C1 inhibitor deficiencies are often detected by investigation of unexpected low C4 levels.

In some long standing SLE patients C4 levels remain low. This does not necessarily denote active disease but more likely genetic C4 deficiency and/or lack of synthesis. However a sudden fall in levels in any individual does usually indicate exacerbation of disease activity. Serial determinations are always a better guide to disease activity.

Although in some long standing SLE patients C3 levels remain low, which does not necessarily denote active disease, a sudden fall in levels does usually indicate exacerbation of disease activity and a risk of renal damage.

Patients with suspected or confirmed systemic lupus erythematosus ("lupus" or SLE) undergo laboratory tests (blood tests) for multiple reasons. Physicians and other health care professionals test patients periodically and use the information derived from the tests in various ways.

ANA is a screening test, since almost all patients with lupus have a strongly positive test. However, a positive ANA test result does not by itself confirm a diagnosis for lupus. About 10% of people who do not have an autoimmune disease, as well as many who have other autoimmune diseases (such as thyroid disease) also have positive ANA tests, but usually less strongly positive. Once a person has a positive result, their ANA generally remains positive, so future ANA tests need not be repeated.

A diagnosis of lupus is based on a combination of symptoms, physical examination abnormalities (for example, a rash or swollen joints), and laboratory tests. Not all patients with lupus have the anti-dsDNA antibody. Patients who have lupus but do not have anti-dsDNA often have a related antibody, anti-Sm.

Anti-Smith (anti-Sm), named for the first person known to have this antibody, is the antibody seen in most patients with lupus who do not have anti-dsDNA. Some patients have both anti-dsDNA and anti-Sm. Anti-Sm antibody binds to a protein that is attached to DNA. Unlike anti-dsDNA, the Sm antibody does not change in titer during a lupus flare or in response to treatment so need not be monitored.

For technical reasons based on the size of the antigens (molecules to which the autoantibodies react), anti-Ro/SSA is subdivided into 60kd (kilodalton, the size of the molecule) and 52kd components, and La/SSB is 48kd. Distinguishing among them is useful in some special circumstances.

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